{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kernel SHAP explanation for SVM models "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "Note\n",
    "    \n",
    "To enable SHAP support, you may need to run\n",
    "    \n",
    "```bash\n",
    "pip install alibi[shap]\n",
    "```\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# shap.summary_plot currently doesn't work with matplotlib>=3.6.0,\n",
    "# see bug report: https://github.com/slundberg/shap/issues/2687\n",
    "!pip install matplotlib==3.5.3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, we show how to explain a multi-class classification model based on the SVM algorithm using the `KernelSHAP` method. We show how to perform instance-level (or *local*) explanations on this model as well as how to draw insights about the model behaviour in general by aggregating information from explanations across many instances (that is, perform *global explanations*)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import shap\n",
    "shap.initjs()\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from alibi.explainers import KernelShap\n",
    "from sklearn import svm\n",
    "from sklearn.datasets import load_wine\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names'])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine = load_wine()\n",
    "wine.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = wine.data\n",
    "target = wine.target\n",
    "target_names = wine.target_names\n",
    "feature_names  = wine.feature_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split data into testing and training sets and normalize it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training records: 142\n",
      "Testing records: 36\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(data, \n",
    "                                                    target, \n",
    "                                                    test_size=0.2, \n",
    "                                                    random_state=0,\n",
    "                                                   )\n",
    "print(\"Training records: {}\".format(X_train.shape[0]))\n",
    "print(\"Testing records: {}\".format(X_test.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler().fit(X_train)\n",
    "X_train_norm = scaler.transform(X_train)\n",
    "X_test_norm = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitting a support vector classifier (SVC) to the Wine dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='intro'></a>\n",
    "\n",
    "SVM, is a binary classifier, so multiple classifiers are fitted in order to support multiclass classification. The algorithm output is explained [here](#ovr_definition)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=1, gamma=0.1, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=1, gamma=0.1, random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(C=1, gamma=0.1, random_state=0)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "classifier = SVC(\n",
    "    kernel = 'rbf', \n",
    "    C=1, \n",
    "    gamma = 0.1, \n",
    "    decision_function_shape='ovr',  # n_cls trained with data from one class as postive and remainder of data as neg\n",
    "    random_state = 0, \n",
    ")\n",
    "classifier.fit(X_train_norm, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model assessment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at confusion matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = 'Confusion matrix for SVC classifier'\n",
    "disp = ConfusionMatrixDisplay.from_estimator(classifier, \n",
    "                                             X_test_norm, \n",
    "                                             y_test,\n",
    "                                             display_labels=target_names,\n",
    "                                             cmap=plt.cm.Blues,\n",
    "                                             normalize=None,\n",
    "                                            )\n",
    "disp.ax_.set_title(title);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The confusion matrix show the classifier is perfect - let's understand what patterns in the data help the SVC perform so well!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply KernelSHAP to explain the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='ovr_definition'></a>\n",
    "\n",
    "The model needs access to a function that takes as an input samples and returns predictions to be explained. For an input $z$ the decision function of an _binary_ SVM classifier is given by:\n",
    "\n",
    "$$\n",
    "\\text{class}(z) = \\text{sign}(\\beta z + b)\n",
    "$$\n",
    "\n",
    "where $\\beta$ is the best separating hyperplane (linear combination of support vectors, the training points closest to the separating hyperplane) and $b$ is the bias of the model. \n",
    "\n",
    "For the 'one-vs-rest' SVM, `nclass` binary SVM algorithms are fitted using each class as the positive class and the remainder as negative class. The classification decision is taken by assigning the label from the classifier with the maximum absolute decision score. Therefore, to explain our model we could consider explaining the SVM model which outputs the highest decision score. Click here to go back to [source](#intro).\n",
    "\n",
    "To do so, the KernelSHAP explainer must receive a callable that returns a set of scores when called with an input $X$, in this case the `decision_function` attribute of our classifier. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_fcn = classifier.decision_function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using 142 background data samples could cause slower run times. Consider using shap.sample(data, K) or shap.kmeans(data, K) to summarize the background as K samples.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "KernelShap(meta={\n",
       "  'name': 'KernelShap',\n",
       "  'type': ['blackbox'],\n",
       "  'task': 'classification',\n",
       "  'explanations': ['local', 'global'],\n",
       "  'params': {\n",
       "              'link': 'identity',\n",
       "              'group_names': None,\n",
       "              'grouped': False,\n",
       "              'groups': None,\n",
       "              'weights': None,\n",
       "              'summarise_background': False,\n",
       "              'summarise_result': None,\n",
       "              'transpose': False,\n",
       "              'kwargs': {}}\n",
       "            ,\n",
       "  'version': '0.7.1dev'}\n",
       ")"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "svm_explainer = KernelShap(pred_fcn)\n",
    "svm_explainer.fit(X_train_norm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='source_1'></a>\n",
    "<a id='f_1'></a>\n",
    "\n",
    "Note that the explainer is fit to the classifier training set . This training set is used for two purposes:\n",
    "\n",
    "   - To determine the model output when all inputs are missing ($\\phi_0$ in eq. $(5)$ of [[1]](#References). Because the SVM model does not accept arbitrary inputs, this quantity is approximated by averaging the decision score for each class, across the samples in `X_train_norm` as shown below and it is stored as the `expected_value` attribute of the explainer\n",
    "   - The values of the features in the $N \\times D$ `X_train_norm` dataset are used to replace the values missing during the feature attribution ($\\phi_i$) estimation process. Specifically, `nsamples` copies of `X_train_norm` are tiled to create a dataset where, for each copy, a subset of features $z'$ of size $s = |z'|$ are replaced by the values in the instance to be explained and the complement of this subset is left to the background dataset value. These background values simulate the effect of *missing values*, since most models cannot cope with arbitrary patterns of missing values at inference time. Therefore, when computing the shap value of a particular feature, $\\phi_i$, `nsamples` regression targets ($f(h_x(z'))$ in eq $(5)$ of [[1]](#References)) are computed as the *expected* prediction of the model to be explained when a given subset of features is missing as opposed to replacing the missing feature with a single value. Note that the averaging operation can be replaced by weighted averaging by specifying the `weights` argument to the `fit` method. <sup>[(a)](#Footnotes)</sup>   \n",
    "   \n",
    "For the above reason, this is sometimes referred to as the *background dataset*; a larger dataset increases the runtime of the algorithm, so for large datasets, a subset of it should be used. An option to deal with the runtime issue while still providing meaningful values for missing values is to summarise the dataset using the `shap.kmeans` function. This function wraps the `sklearn` k-means clustering implementation, while ensuring that the clusters returned have values that are found in the training data. In addition, the samples are weighted according to the cluster sizes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1.11022302e-16  4.44089210e-16 -4.44089210e-16]\n"
     ]
    }
   ],
   "source": [
    "# expected_values attribute stores average scores across training set for every binary SVM \n",
    "mean_scores_train = pred_fcn(X_train_norm).mean(axis=0)\n",
    "# are stored in the expected value attibute of the explainer ...\n",
    "print(mean_scores_train - svm_explainer.expected_value)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "727d5a8223c94efabf974a91ca48bac7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/36 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "svm_explanation = svm_explainer.explain(X_test_norm, l1_reg=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='source_2'></a>\n",
    "\n",
    "In cases where the feature space has higher dimensionality, only a small fraction of the missing subsets can be enumerated for a given number of samples `nsamples`. If the the fraction of the subsets enumerated falls below a fraction (`0.2` for version `0.3.2`) and the regularisation is set to `auto`, a least angle regression with the AIC information criterion for selecting the regularisation coefficient $\\alpha$ is performed in order to select features. The regularisation has no effect if the fraction is greater than this threshold and `l1_reg` is not set to `auto`. Other options for regularisations are:\n",
    "- `l1_reg=\"num_features(10)\"`: in this case, the LARS algorithm [[2](#References)] is used to which 10 features to estimate the shap values for.\n",
    "- `l1_reg=\"bic\"`: in this case, the least angle regression is run with the Bayes Information Criterion \n",
    "- `l1_reg=0.02`: if a float is specified, the $\\ell_1$-regularised regression coefficient is set to this value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Local explanation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the SVM algorithm returns a score for each of the $3$ classes, the shap_values are computed for each class in turn. Moreover, the attributions are computed for each data point to be explained and for each feature, resulting in a $N_e \\times D$ matrix of shap values for each classs, where $N_e$ is the number of instances to be explained and $D$ is the number of features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output type: <class 'list'>\n",
      "Output size: 3\n",
      "Class output size: (36, 13)\n"
     ]
    }
   ],
   "source": [
    "print(\"Output type:\", type(svm_explanation.shap_values))\n",
    "print(\"Output size:\", len(svm_explanation.shap_values))\n",
    "print(\"Class output size:\", svm_explanation.shap_values[0].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a given instance, we can visualise the attributions using a force plot. Let's choose the first example in the testing set as an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The predicted class for the X_test_norm[0] is 0.\n",
      "OVR decision function values are [ 2.24071294  0.85398239 -0.21510456].\n"
     ]
    }
   ],
   "source": [
    "idx =  0\n",
    "instance = X_test_norm[idx][None, :]\n",
    "pred = classifier.predict(instance)\n",
    "scores =  classifier.decision_function(instance)\n",
    "class_idx = pred.item()\n",
    "print(\"The predicted class for the X_test_norm[{}] is {}.\".format(idx, *pred))\n",
    "print(\"OVR decision function values are {}.\".format(*scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that class $0$ is predicted because the SVM model trained with class $0$ as a positive class and classes $1$ and $2$ combined as a negative class returned the largest score.\n",
    "\n",
    "To create this force plot, we have provided the plotting function with four inputs:\n",
    "- the expected predicted score by the class-0 SVM assuming all inputs are missing. This is marked as the base value on the force plot\n",
    "- the feature attributions for the instance to be explained \n",
    "- the instance to be explained \n",
    "- the feature names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <b>Visualization omitted, Javascript library not loaded!</b><br>\n",
       "  Have you run `initjs()` in this notebook? If this notebook was from another\n",
       "  user you must also trust this notebook (File -> Trust notebook). If you are viewing\n",
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   ],
   "source": [
    "shap.force_plot(\n",
    "    svm_explainer.expected_value[class_idx], \n",
    "    svm_explanation.shap_values[class_idx][idx, :] , \n",
    "    instance,  \n",
    "    feature_names,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The force plot depicts the contribution of each feature to the process of moving the value of the decision score from the _base value_ (estimation of the decision score if all inputs were missing) to the value predicted by the classifier. We see that all features contribute to increasing the decision score, and that the largest increases are due to the `proline` feature with a value of `1.049` and the `flavanoids` feature with a value of `0.9778`. The lengths of the bars are the corresponding feature attributions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, below we see that the `proline` and `alcohol` features contribute the to decreasing the decision score of the   SVM predicting class `1` as positive and that the `malic_acid` feature increases the decision score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "  <b>Visualization omitted, Javascript library not loaded!</b><br>\n",
       "  Have you run `initjs()` in this notebook? If this notebook was from another\n",
       "  user you must also trust this notebook (File -> Trust notebook). If you are viewing\n",
       "  this notebook on github the Javascript has been stripped for security. If you are using\n",
       "  JupyterLab this error is because a JupyterLab extension has not yet been written.\n",
       "</div></div>\n",
       " <script>\n",
       "   if (window.SHAP) SHAP.ReactDom.render(\n",
       "    SHAP.React.createElement(SHAP.AdditiveForceVisualizer, {\"outNames\": [\"f(x)\"], \"baseValue\": 1.4171025278703537, \"outValue\": 0.8539823861264118, \"link\": \"identity\", \"featureNames\": [\"alcohol\", \"malic_acid\", \"ash\", \"alcalinity_of_ash\", \"magnesium\", \"total_phenols\", \"flavanoids\", \"nonflavanoid_phenols\", \"proanthocyanins\", \"color_intensity\", \"hue\", \"od280/od315_of_diluted_wines\", \"proline\"], \"features\": {\"0\": {\"effect\": -0.18264964205364848, \"value\": 0.9388470699160594}, \"1\": {\"effect\": 0.0587525385058649, \"value\": -0.6321660679120147}, \"2\": {\"effect\": 0.015010914822269295, \"value\": -0.4350103029406882}, \"3\": {\"effect\": -0.10449593014661213, \"value\": -0.9196956154312363}, \"4\": {\"effect\": -0.021228020126666886, \"value\": 1.263240410536125}, \"5\": {\"effect\": -0.042241855344956586, \"value\": 0.5599986330691132}, \"6\": {\"effect\": 0.0004442707399767154, \"value\": 0.9777541576072868}, \"7\": {\"effect\": -0.04867053587768441, \"value\": -1.2063753302691222}, \"8\": {\"effect\": -0.020661592822415642, \"value\": 0.02366801917662191}, \"9\": {\"effect\": -0.040988729300311444, \"value\": 0.3392846951968553}, \"10\": {\"effect\": 0.005689413661779691, \"value\": -0.14557480491870503}, \"11\": {\"effect\": 0.011398585324990967, \"value\": 0.8522954133614274}, \"12\": {\"effect\": -0.19347955912652792, \"value\": 1.0494052626762695}}, \"plot_cmap\": \"RdBu\", \"labelMargin\": 20}),\n",
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     "execution_count": 17,
     "metadata": {},
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    }
   ],
   "source": [
    "shap.force_plot(\n",
    "    svm_explainer.expected_value[1], \n",
    "    svm_explanation.shap_values[1][idx, :] , \n",
    "    instance,  \n",
    "    feature_names,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An alternative way to visualise local explanations for multi-output models is a _multioutput decision plot_. This plot can be especially useful when the number of features is large and the force plot might not be readable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def class_labels(classifier, instance, class_names=None):\n",
    "    \"\"\"\n",
    "    Creates a set of legend labels based on the decision \n",
    "    scores of a classifier and, optionally, the class names.\n",
    "    \"\"\"\n",
    "\n",
    "    decision_scores = classifier.decision_function(instance)\n",
    "    \n",
    "    if not class_names:\n",
    "        class_names = [f'Class {i}' for i in range(decision_scores.shape[1])]\n",
    "    \n",
    "    for i, score in enumerate(np.nditer(decision_scores)):\n",
    "        class_names[i] = class_names[i] + ' ({})'.format(round(score.item(),3))\n",
    "        \n",
    "    return class_names\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "legend_labels = class_labels(classifier, instance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x482.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "r = shap.multioutput_decision_plot(svm_explainer.expected_value.tolist(), \n",
    "                                   svm_explanation.shap_values,\n",
    "                                   idx, \n",
    "                                   feature_names=feature_names, \n",
    "                                   feature_order='importance',\n",
    "                                   highlight=[class_idx],\n",
    "                                   legend_labels=legend_labels,\n",
    "                                   return_objects=True,\n",
    "                                   legend_location='lower right')    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The decision plots shows how the individual features influence contribute to the classification into each of the three classes (a *prediction path*). One sees that, for this example, the model can easily separate the three classes. It also shows, for example, that a wine with the given alcohol content is typical of class `0` (because the `alcohol` feature contributes positively to a classification of class `0` as negatively to classification in classes `1` and `2`)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the feature ordering is determined by summing the shap value magnitudes corresponding to each feature across classes and then ordering the `feature_names` in descending order of cumulative magniture. The plot origin, marked by the gray vertical line, is the average base values across the classes. The dashed line represents the model prediction - in general we can highlight a particular class by passing the class index in the `highlight` list."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Suppose now that we want to analyse instance `5` but realise that the feature importances are different for this instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx =  5\n",
    "instance = X_test_norm[idx][None, :]\n",
    "pred = classifier.predict(instance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "instance_shap = np.array(svm_explanation.shap_values)[:, idx, :]\n",
    "feature_order = np.argsort(np.sum(np.abs(instance_shap),axis=0))[::-1]\n",
    "feat_importance = [feature_names[i] for i in feature_order]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['flavanoids', 'alcalinity_of_ash', 'od280/od315_of_diluted_wines', 'alcohol', 'ash', 'total_phenols', 'proline', 'magnesium', 'proanthocyanins', 'hue', 'malic_acid', 'nonflavanoid_phenols', 'color_intensity']\n"
     ]
    }
   ],
   "source": [
    "print(feat_importance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to create a multi-output decision plot with the same feature order and scale. This is possible, since by passing the `return_objects=True` to the plotting function in the example above, we retrieved the feature indices and the axis limits and can reuse them to display the decision plot for instance `5` with the same feature order as above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x482.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.multioutput_decision_plot(svm_explainer.expected_value.tolist(), \n",
    "                               svm_explanation.shap_values,\n",
    "                               idx, \n",
    "                               feature_names=feature_names, \n",
    "                               feature_order=r.feature_idx,\n",
    "                               highlight=[pred.item()],\n",
    "                               legend_labels=legend_labels,\n",
    "                               legend_location='lower right',\n",
    "                               xlim=r.xlim,\n",
    "                               return_objects=False) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Global explanation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As shown above, the force plot allows us to understand how the individual features contribute to a classification output *given* an instance. However, the particular explanation does not tell us about the model behaviour in general. Below, we show how such insights can be drawn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Stacked force plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The simplest way we can do this is to stack the force plot for a number of instances, which can be achieved by calling the `force_plot` function with the same arguments as before but replacing `instance` with the whole testing set, `X_test_norm`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id='i627GW3DDOY0339HQITJI'>\n",
       "<div style='color: #900; text-align: center;'>\n",
       "  <b>Visualization omitted, Javascript library not loaded!</b><br>\n",
       "  Have you run `initjs()` in this notebook? If this notebook was from another\n",
       "  user you must also trust this notebook (File -> Trust notebook). If you are viewing\n",
       "  this notebook on github the Javascript has been stripped for security. If you are using\n",
       "  JupyterLab this error is because a JupyterLab extension has not yet been written.\n",
       "</div></div>\n",
       " <script>\n",
       "   if (window.SHAP) SHAP.ReactDom.render(\n",
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       "    document.getElementById('i627GW3DDOY0339HQITJI')\n",
       "  );\n",
       "</script>"
      ],
      "text/plain": [
       "<shap.plots._force.AdditiveForceArrayVisualizer at 0x7f1dda9f2dc0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_idx = 0 # we explain the predicted label\n",
    "shap.force_plot(\n",
    "    svm_explainer.expected_value[class_idx], \n",
    "    svm_explanation.shap_values[class_idx], \n",
    "    X_test_norm,  \n",
    "    feature_names,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the default configuration, the `x` axis is represented by the `36` instances in `X_test_norm` whereas the `y` axis represents the decision score. Note that, like before, the decision score is for class `0`. For a given instance, the height in between two horizontal lines is equal to the shap value of the feature, and hovering over a plot shows a list of the features along with their values, sorted by shap values. As before, the blue shading shows a negative contribution to the decision score (moves the score *away* from the baseline value) whereas the pink shading shows a positive contribution to the decision score. Hovering over  the plot, tells us, for example, that to achieve a high decision score (equivalent to class `0` membership) the features `proline`  and `flavanoids` are generally the most important and that positive `proline` values lead to higher decision scores for belonging to this class whereas negative proline values provide evidence against this one belonging to `0` class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see the relationship between decision scores and the values more clearly, we can permute the `x` axis so that the instances are sorted according to the value of the `proline` feature by selecting `proline` from the horizontal drop down menu."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<div style='color: #900; text-align: center;'>\n",
       "  <b>Visualization omitted, Javascript library not loaded!</b><br>\n",
       "  Have you run `initjs()` in this notebook? If this notebook was from another\n",
       "  user you must also trust this notebook (File -> Trust notebook). If you are viewing\n",
       "  this notebook on github the Javascript has been stripped for security. If you are using\n",
       "  JupyterLab this error is because a JupyterLab extension has not yet been written.\n",
       "</div></div>\n",
       " <script>\n",
       "   if (window.SHAP) SHAP.ReactDom.render(\n",
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       "    document.getElementById('iAFDGX4JS0XF8VI81XS0P')\n",
       "  );\n",
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      "text/plain": [
       "<shap.plots._force.AdditiveForceArrayVisualizer at 0x7f1dda98bfd0>"
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     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "shap.force_plot(\n",
    "    svm_explainer.expected_value[class_idx], \n",
    "    svm_explanation.shap_values[class_idx], \n",
    "    X_test_norm,  \n",
    "    feature_names,\n",
    ")"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also explore the effect of a particular feature across the testing dataset. For example, in the plot below, by selecting `flavanoids` from the top drop-down, the instances are ordered on the  `x` axis in increasing value of the `flavanoids` feature. \n",
    "\n",
    "Similarly, selecting `flavanoids effects` from the side drop-down will plot the shap value as opposed to the model output. The effect of this feature generally increases as its value increases and the large negative values of this feature reduce the decision score for classification as `0`. Note that the shap values are respresented with resepect to the base value for this class (0.798, as shown below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<div style='color: #900; text-align: center;'>\n",
       "  <b>Visualization omitted, Javascript library not loaded!</b><br>\n",
       "  Have you run `initjs()` in this notebook? If this notebook was from another\n",
       "  user you must also trust this notebook (File -> Trust notebook). If you are viewing\n",
       "  this notebook on github the Javascript has been stripped for security. If you are using\n",
       "  JupyterLab this error is because a JupyterLab extension has not yet been written.\n",
       "</div></div>\n",
       " <script>\n",
       "   if (window.SHAP) SHAP.ReactDom.render(\n",
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       "    document.getElementById('i7EXVA5LHH96O7IOJD6KL')\n",
       "  );\n",
       "</script>"
      ],
      "text/plain": [
       "<shap.plots._force.AdditiveForceArrayVisualizer at 0x7f1dda8faac0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shap.force_plot(\n",
    "    svm_explainer.expected_value[0], \n",
    "    svm_explanation.shap_values[0], \n",
    "    X_test_norm,  \n",
    "    feature_names,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.79821894 1.41710253 0.69461514]\n"
     ]
    }
   ],
   "source": [
    "print(svm_explainer.expected_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To visualise the impact of the features on the decision scores associated with class `0`, we can use a summary plot. In this plot, the features are sorted by the sum of their SHAP values magnitudes across all instances in `X_test_norm`. Therefore, the features with the highest impact on the decision score for class `class_idx` are displayed at the top of the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x482.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(svm_explanation.shap_values[0], X_test_norm, feature_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, the `proline` and `flavanoids` have the most impact on the model output; as the values of the features increase, their impact also increases and the model is more likely to predict class `0`. On the other hand, high values of the `nonflavonoid_phenols` have a negative impact on the model output, potentially contributing to the classification of the particular wine in a different class. To see this, we do a summary plot with respect to `class_2`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EcVnROT9kNf/U6vJ78E3oh49debP9eLM7KXgY0nnnNyUrVms7h8sHvn6m2bidtNz4xpcMyIt9K3JG9q7LUxMsxuc2BiWJPosJefEF1ZZlMTiv9UtQRraXN1bbeMttjh21a5ler4u8Qbs/pgSPxfj+ex7kj87cs+v7sIzYgVZOkkVOi3+ZvBSLvDgHJne23prWkK4o0V3dC/wNKAXOBk7SWpfH2lBr/QTOAN/HgGLgSuDEdgwcbstvgJeAl5RSFcBqnMHN8v8jxF5y0XNBTno0wOyHAlz9SrDtHYSIkq4o0e1Eu6Ju0lo/1tVt6QbkH1S027x58wCYM2dOF7dkzxhj8Py6tmHseWYilPxy99nBPqpDqZ6wdVnM9xePua9Hd0XJJ04hhBDdimVZTM5rvLZOyZNL1d7QW++KkjE2Qgghup3XLvTzx49CeN3w88O7y1cQiJ5AAhvR7With3V1G4QQXWtAqsVfTtp7A6wF9IZbu2OR/J4QQggheg3J2AghhBB9UG8YTxOLBDZCCCFEn9Q7AxvpihJCCCFEryEZGyGEEKIP6q1dUZKxEUIIIUSvIYGNEEIIIXoN6YoSQggh+iDpihJCCCGE6OYkYyOEEEL0SZKxEUIIIYTo1iRjI4QQQvRBvXWMjQQ2QgghRJ/UOwMb6YoSQvQMtr3b1Sa63rSxXVuMbTq0v3AY03nn0e7EskTvJ4GNEKJ7C4bglN+B9yw45BdQWtVstbFtKs57miLvbygZ8DsiCVdgD7oO88WGdlVTsbSUdya8wKvZT7LitiWdeAB9zz0L6kj/WQn5vyzhozWhPS7n4y02/f8WIvFPYf76eaQTWyjA6YqK9erpJLARQnRv//sY5mknY/PpSvjHa81WB19bReCJr8E2RApqqA0nwdZSzPVPt6ua5b9ZTO3mGkzYsPqOpVStqejMo+gzqgKGa5+rJhCGomrDdc9X73FZ17xjU1gDwQhc955NaZ1kbkTbJLARQnRvXs9u5y2vu8UO0YvfLst3z/I2eTu0wOWRt8c94bLA3eTUtfPX0EzTfV0WuHt+MkHsA/KfK3oMpdQGpdT5nVDOMKWUUUoN6kAZHyilbupoW0QczpwBFx0JWSlw0nT48YnNVvuOG43/RwdjZSXiHZNBYraBSYOw7p7brmom/nYaaVMy8WUnMOG300galtKZR9FnJPks/js3hf6pFqNzXPzjzD0/j/881s24bMhNgv8e7yYtQSKbztRbu6LkrighRPfmdsNDP97tJql/P5nUv5/coWqSR6Yya8GJbW8o2jRXJTBXJXS4nP3zLJZf7u2EFom+RAIbIcS+8+wnMH85HL8/nDAt7t0qnlxB7cLtpMwZQfIxQ5uv/GodPPw+ZkQegRo/7lcW4h6VievXZ8CIPHj1C3hrCcyaAGccHLN8YxsK/rWMurUV5F40huSp/eJuW1VBLUsfXYsv2cPkS0ax6cMdFHxRzKDD+pNVVE7th1tImj2U5JNGNtsv8MBn1P1rEdbwLEpGjcK2LQZdux++vKS4626LHTEsnFdIWWGQ6cf1w2yvYtObW8neL5Mhx+fz9YNrCNWGmXThSJL7J+6yf12dzduvlBIM2hx9QiYZma1fMgIBmxfeqqSq2qYgxUdpxOKKQxIYnePm9UW1LNsQYtYUPweM9fHCVwEWrgsze4KXo8f7qAvYvPBWFbV1NnOOSubF9RYvLg+T7DZcMt3LCWOdeisChrs+jRCIwHUHu8lN7lh2wRjDPUsMK0sM509wofIay3tpcYCP14SpSfDi8bu4aJKL/fMaOzkWbTc8sdxmfLbF9ydbWFZPzHT0xDa3zerMW/KE6Cil1E+AK4F8oBR4HLhJax1RSm2ITj8W3XYycAcwHXADX2qtj2my7i/A/tFyHgB+Hy1nGLAeuBD4BTAY+BS4SGu9Pbp/NnA3MDvatDeBa7XWJdH1HwDvaK1v31vnIqr3/IPO+xxO+b0z7XLBR7fBzPFt7lbxv5VsP+dVZ8ZtMXTRufin9Xfmt5fAuKuhogaAMAl4CAJgBmVjPfgjmH0b1L/PvXZjzIBq861fsPk3XzhVpPvYf+VZ+Pq3HWAY2/D0Ce9QvtEZIJs3PYuCL0oAyK6pZvLm7c6GFgz68BwSD3N6P8NvLKf8hEcw0dEANSSwgXyS9stEfXNGm/W2Zd68eQAkFE1j/jMFAKR5wvRfvgUTds5F+pQsClc4A6QzRqZy1qtH71LOP+/ayhLtHNuAfB+3/GlYq3XeeU8R8z+vBaDKZfF+ShJ5aS7+fVwCtz/m1ON2wWWnp/Czl+sAZ9zMW9ek8e4bFczXzr4leUl86ooGWcZghSPM/0EiM4d5OOGJIG+sddq/f57Fl5f79vgcAdyxyOaGj5zHA6R4Yfn33AxKtXhzWZCz762kLtlHIMXJPKUlwIofeBmQYrGx3DDhwQg1YaecPx/h4lrVJSM7OhSZVFnXxnx/STF39+iIR8bYiO5mC3ACkAacCnwPuKzlRkqpAcCH0dcwIA/4Q3RdOvA28H50+UnRcq5rUczZwOE4QVQycGuTdY8DmcD46Ksf8GjHD699Kisre8104ONvGw/MtuGr9XHtW6cLGveLGMoXbm6YrVm8tiGoAXDReGuxtaUYPlrWGNQAgU+Wx6yrbGFjHZHyIHXRO6LaaluwMtQQ1ACUrG68kyq1JtDYbgN1X+5o2Df86caGoAYgIRqM1Swtxa4Ld9o537SisT12cV1DUANQsa7J8a+tJFSza70b1zUew/atQQIBu9W6Vq1v3DbFNniBgkrDp8sbz0/EhkWrw41tMvD1lggrm+y7qa7pIG5nzMdX25x69fbG9i8uMIRt06Hzs3BLsGG6KgQrS5zyP1vj/E1FmgwgrwjAki3OsSwvMQ1BDcCnTcrZl/9TIjYJbES3orV+Tmu9XmtttNZf4QQTu36UhAuANVrr32utq7XWQa31O9F1JwFB4HatdUBrvRz4I7sGSLdorYu01hXAE4ACUEoNBI4DrtNal2qtS3GCohOjAdU+k5qa2mumE747E/zRT9hpSTB7alz7ppw6Csvn3B7jyvKTdeLohm2SZk6EUc6vxFgQpjHLYg4eA2fNhNTop/9EHwlnzIhZV/+5jWX6x6STPCU7rrYlpPvIPySnYfmwowbgTnDeVsty08DvdKG4Un0kHzesYV/v6ZNwuRofJFgZbXfWnCG4/J5OO+dTjmjsUksek44/28k+WB6L/MP7N6wbfHh/vEm71qsObhz4u9/UZBISXK3WdeiByQ3TO91uQpbF9EFuzpiZiid6d1N6ssWZhySQEO3RSk+0OHKcl8MPaNx3Rj8bV32+wBhSvIbZo522nTm+8ZJ1+jgXHpfVofNzzgRfQ8pjWBpM7+/MnTItBb8XvIFwQ2A8KhNmDnfaedAAi6Fpzn4uC86e4ItZ/t6e7igZPCzEPqCUmosTRIzA+fv0AQtjbDoMWNVKMYOBjVrrpmnWtdHlTW1vMl0N1L9j1G+3vsX+9eua7ifipUbBV3+Cz9fAoeNheP+29wGSDs1n6JfnUfdlIUmzBuEdkta4Mi0JFv4B3vgSa2QerqCX0Btf4RmVhXXOIZCUAF/dCZ+shANHwdj8mHXkXjCGxFHp1K2rIOOEwbhT4h+wevw9B7Ph3QK8yR6GHN6fqd8fQ+E3peRNz8ZfHaDu8wISDxmId0RGwz7uqfmkf301gXsW4Zo8AGtgHuk1EbJPG9p6RXvggBNyyB2SSFlhkLEHphOpHEvBp4Vkjksna3wGoz8sIFwbYehRseP1sy7IZdzEJIJBw1S1+7ubLvpOBvuNSaCm1lCX6qGoFk6Z6CU5weKxX2SzanOIaaN95GW5+ehnHhZvjnDwCA9Ds90M/246+431UVtnOHhqIl/vNLy7NkySG04a52d4lhPQ/PMEDyeMsgmE4bRxHf9cftY4F8PSLVaXGo4bZpHhdy7qUwd7mP+zDL7cFMaX5KbGWBw/wkVq9K6sTL/FovPcvLXRMDbT4oABPT8Y6E0ksBHdhlJqMPAYcAbwutY6qJT6E9FMSgsbgO+2UtRmYKhSymoS3IyILo9H/XbDgDVN9m+6TuyJcYOcVzslTOyHJ8WDlRnjTpvsVDhvFhB9Qzt8RPP1I/OcVxtSZ/QndUZ8wVZTbp+bkSc0BkwZI1LJGFEfIyfjG5sVe7+JA0j6+6kA+Ntda/yGTkxh6MT65iQy8vTG4GnIrLbPy+Rp8d+uPX3SrgOQAUYN9DBqYOPlZmyeh7F5zS8/qsm+0wdaTB+46/gZy7KYM6YDD8aJ4cABFgfGCExG93czun/rdeUmW5w/QQKa7ki6okR3koLzN7kTCCmlDsbpcorlMWCsUuoGpVSSUsqnlDomuu5VIAG4Mbp8LHADcH88jdBabwPeAu5SSmUopTKBu3CCLcnW7GPGGMrOeZqiYX+mKP9PBOdv6OomCdEr9NauKAlsRLcRHQvzG+AloAz4OfBkK9tuA44AjsUZcFwA/Cy6rhznbqZjgB04dzQ9Avy5Hc05H6gEVgIrou25sF0HJDpFeEkBgf8tBcBUBqj+/fwubpEQojuT272F6N76/D9oZFMZRSP/AmFnsK3/oqmkP9TxW6J7s/rbvefMmdPFLRF7WYfSKxXWT2O+v6SZP/XotI1kbIQQ3Zp7SAZpj5yB54B8Er47kdQ/HdfVTRJCdGMyeFgI0e0lzp1M4tzJXd0MIXqV3jCeJhYJbIQQQog+qLcGNtIVJYQQQoheQzI2QgghRJ8kGRshhBBCiG5NMjZCCCFEH9RbnyUhgY0QQgjRB8ngYSGEEEKIbk4yNkIIIUSfJBkbIYQQQohuTTI2QgghRB/UW8fYSGAjhBBC9EG9NbCRrighhBBC9BoS2AghhBCi15DARgghhBC9hoyxEUKIbsgYw6K/LGfLgkIGHtiP4acO4YN/rAXgyKtH0X9Maqv7lm7ys/WLVEq+WMupVw0mI8fXZn2hoM1TDxayeUOAA2amcuzJWdQFbB56pJjNW4IcfmgKxx2b3qFjWrUzwlUv1lETNPzhRD+HDZdLUFfqrWNs5K9KCCG6obWvbWXxvasBKPq2nG/e3klFnXMhevmmZVz+9MEx9wvURFj9dhZ22MWqwgpe/McmLr5lVJv1vTWvlI/frwBg0/oAw0b6+XJZHR8tqAJg/YYSRo30M3JEwh4f0wVP1bJocwSAUx+qoeg3qbhcvfPi2hP01sBGuqKEEKIbqi0JNJsPVoYapmtKQy03bxCos7HDjW/t1WXhuOqrrIjsMl/RYlnL+fYqrLIbpsvqDMGOFSdETBLYCJRSDyml7tuL5VcppWbsrfI7Sil1j1LqH13dDiGaGn3yYDJHOd1N6UOTUT8Y7Two1oLDvj+81f3Ssrz0n+hkWTw+i6POHRBXfUcel0F6phuAUWMTmbR/MifMTictzblMTJzgZ9J+iR04Irh1th9P9Krz66MT8Ht7Z8ag57BaefVsljG99fs9RbyUUg8BYa31ZV3dFgCllAEO01ov6KL6NwA3aa0f64r6W5B/0D4sErKp3lFLcm4ibp+L6tIgAMmZux8zM2/ePAJVbk6aM5vElPhHHASDNhVlEbL6eRq6iAIBm4qKCNnZnk7pNiqutgmEYWC6fK7uBB36hRRav475/pJrbu3R0Y2MsREdopSyALfWOr58t+hbbBte/hKMgVOmQ2UtvKphWC7MHN/u4oLzN2BvKsN38jhc6f7GFZt2wofLYP/hsN/QTjwAhx222fBeAS6PxdAj87Csjr/vR2rCbH14DYGNVaSPScGT4CJl9mA2flhIwaZahh6fz5AJqWxYVELx2iqmH5lG2vJ1MHkIW7dnUbq1jhEHZpCU7qVwXTU71tbQL8tFcF0FVcssjB+8nsZ2bvmggFB1mCHHDMCd4I7Zpu2bA+zYFsTnSyYtw7k8rFsboKwsTEpKMomJrR/3yi0hVm0LMz7fw7rNIfJzPUwc4eONlWHKA4bTJnh4d6OhKgSnjXbKeWNNhPI6OG2ciwTPrmXft8Tmix021ykXo7PaHwitLzMs2GpQeRbjs53yFxcavtlpOHqoxcCU9v8e52+22VQBJ4+0SPf36Ot/r/3UJIFNL6KUSgFuBs4AcoDNwA+AL4DfR5cnAguAq7XWm1opZyjwN2AmUAs8B/xCa10bXW+Aa4ALgInAkcDC3bSrIQOjlLoYuCla/vVAMvA08EOtdUQptSS621tKKRt4Smt9mVIqCbgV+A6QDiwCfqS1XhOt44PocQ4DZgOFwHVa65ei6/cH/g5MAiLACuAkrXVp04yVUmoeMAS4Tyl1D/AJcDfwCJCvtQ5Gy0sFtgMnaK3nt3bsfd737oWHo6fnnINhyWpYvsWZv++HcOkxcRdV+59FVF3xMgDu/fqTuegKrEQvbCyEaT+FkirweuDdm+GwCZ16GO/8n2b9W9sBmHD2MA67eUqHyjMRm89mvUGt3kkCYSpxxsxE0hNYnZKKcVl8/cgGsg7OYacuIiVYw0G/fBlCdRi3iw8nz2FD5hAyB/o59kfDeOY3q7DDBnckQnZBKdXpTpfRCxu+4rv/mo6+YylL710FwMBDcznukcN2adOSzyv5711bMQYysj384o/D+eTjSp54rBiAoUN9/OqWQXhiBCALVwa58p9lhG3wWJASjOCxYLRK5r9rnYBkcJ7N5oAzPXuYxQH9DL+d7wyyOXKYi3cv9DYLGK96K8y/vnQuvfcvjrDuChiUFn9ws6bUoB6LUB6ABDd8dI6b0jrDSc/bRAz0T4LFF7nJS44/OPnPVxGueNMZJ7RfDiy60ENiD+5Ok8HDoie4HzgIOBpIA07BufjeDRwcfQ0FioB5SqldPrYppTzAq0BBdNuDcQKcP7XY9FLgbCAF+Kqd7RwK9AdGAgcAZwLnAGit668Ys7XWKU26x/4LjIu2Jw/4DHhFKeVtUu5FwF04gc8/gIejARHAP4G3gKxo3dcBwZYN01rPATYBl0Xrnw28CVQDpzbZdC6wWYKaNjyzqHH6uUWNQQ3As5+2q6jAM0sbpiNLdxBesdOZee8bJ6gBCIXhpUUx9t5zdsSw/u3tDfPr3tzW4TJrN9dQ+WUxFuClcQStuzxAQshJfqZU1LDzmzIsYGBVAUmhOgCsiM2YwnUAlG6rY8kbO7HDTgAQcbupTWm8a2nr4jJqSoJseL3xvG9bUEiwYtfBx4s/q6R+ZEJZcZgNq2v5fFF1w/qNG4MU7og9aPndJQHC0XHBYQNhy8IY+Hp54wDozTWNF9G3NhieWtY4kPj9DTbFtc3LfGFVYz4hZMPzq9qXX3hrg6E8Wn0gAvPW2rywxhCJFrOjBuZvaV+Zz6xo3H7pTlhR3K7dxT4igU0voZTKBc4CrtBar9dam2g2Yx3OBf8mrfVWrXU1TrZlPHBgjKIOBEbjZDuqtdZbcTIs34t2O9X7k9Z6rdY6orUOxChnd2qBX2utA9E2vguo3RxbP+BcnKzOjmjW5BZgAE4gV+9/WutPtNY2cC9OgDM6ui6Ik4kZrLUOaa0XRs9Fm6Ll3YcTzNW7NLpsr6qsrOzR0+H9hzROTx4MmSmNB6dGtqtMj8pvmLeyk3APy3S2mTocPE1idDWqU4/F5bboN77x+S399svocJkJeYn48hIxQKTJ23DE4yIUPZY6v4/EXKe7rTgxk4jVuN32tP4AJCS7GTo1rfHYjcEbaOwVTslLwJ/hpd+kzIZlacNTqKMxiqhv25CRjV17Xp9FakaIYcMbg6S0dDeZWZ6YxzVxSNPkv8EdjZBycxqXJ7sbg4IxmbB/TmNANzzdkOlvUWZO82zC9KyaXerd3fTY1BqaDglSeRbT+zcuSHDDiMT2lTk5uzGwy/IbhmXEv+/emO4ogxXz1dNJV1TvMSz6c1WL5TlAArC+foHWukopVQgMBlp+bB4M7Gxx0V8L+KNlFUaXbehAWwu11k1v9KwGWn/aGNTfAvK1Us3iH2+0vfUaPlZrrauj29aXewnwK2CBUioEPAbc0o6xQfcDv1JKDcEJmKYCJ8W57x5LTU3t0dOeeT+Du14FA57rToBtJfDAuzC8P1x1fLvKNL89FldeKvamMvyXTseVmej8cvdPhTd+BfM+hwNHwzmHNvtj6oxjOfG/M/j6obW4vC4mXzyShFTvHpXTdPqg+Sew+pdfEtxUReogH4kDE0k+bQR1r26ncGuAkacPY9rJ/Xnr5qVUbE1n26U/ZPCmNZipwxiYOQb/1jomHZND3pgU/Cketq2sIstjY61L4JttBZhMF9/5+UzcHheH3anIHJtOqDrMhItHkZyWuEt7jjg+E5/PxfYtAabPSGPoiETyhxiysjyUlYY58ug0EhNdNP1Xrd/3tIMTcVmwfHOYsXlutmwLMyjXzbEzEvnLJ2HKag2XHeDh6dVQGYSfTHeR5fcw9dMIZXWGqw/y4I5GIfVlvnGmi/Pm2awqNfz0QDczR7TvPB89KoXXzrB5bb3h0HyLU0c5gaHX5Yyz+c4YF9MHpbSrzDuP8TMk0xljc+lkF5l+K+5998Z0x/X8ICYWCWx6jw3Rn6OBb5ss3wkEcAKf+vEoKUAuzhicljYDOUqpJK11/ceZEUBdtKx69i57dp6W+eGN0Z+jtdY7W24cD631euB7AEqpSTjdUuuBB2Jsvsuxaa23K6VexQmQMoEXtdZFe9KWPiUzGW4/q3E+OxXu/t4eFWW5XSRdc0jslUdPdl57SWJWAgdd17njdpJGpDLlyVm7LD9s1sBm8yf/oel4nplYwPQW++x3dA77HZ3TML9t3jzAJm2AE8B4Ej1M/fHuB2tblsXMozOaLfN4LE44KSPm9i2dclAipxy06/JfHtl4B9evclqsO7z1S5Db5eKpUzvWqXDccBfHtbgz/uL99rxMt8vimgNiD7wW3Yd0RfUSWutC4FngX0qpYUopSyk1CicoeQS4TSk1MDrm5C6cwbOxBiMswgmA7lJKJSmlBgK3AQ9qrffVIPoCGruQ6o/tCZxjywdQSmUopU6PBmltUkpdFD0WgDIgDE0GN+ym/ibuxQmOzscZ8yOEED1Wb+2KksCmd/kesBj4EKgEXsIZaHstoIHPcQbGDgBOadEdBEC0a+ZkYFB020U4A3V/uveb3+CXwK1KqVKl1H+iyy4HVgIfKKUqgW9wBh3HG2wdBXyhlKrG6X57Ani0lW1vB86P1v96k+Vv4WRzynHGBQkhhOhm5AF9QrRD9Lbyt7TWv9tHVco/qGi3efPmATBnzpwubonYyzqUXtlq3Rrz/SXf/LpHp21kjI0QcVJKHU7j7elCCNGj9YZup1gksBEdppRahvNsmpY2aq0n7uv27A1Kqc+BUcCP93QAsxBCiL1PAhvRYb0leNkdrfUBXd0GIYToXL0zYyODh4UQQgjRa0jGRgghhOiDZIyNEEIIIXqN3nrLpXRFCSGEEKLXkIyNEEII0Qf11q4oydgIIYQQoteQjI0QQgjRB/XWjI0ENkIIIUSf1DsDG+mKEkIIIUSvIRkbIYQQog+S272FEEIIIbo5ydgIIYQQfZAMHhZCCCFEryGBjRBCiG6rvNbw+3frqA4apgR8JH9ax4LPFzP+3OFkjU2j+J6l1H61k4yzRpF6zJC4yqz6eDslj6wgYVwmuT+ZguXq2IXQ2DY1/1hEeNlO/OdOImHWsA6VJ0QsEtgIIUQvcOGT1by8LAzApcvTOHh1DStYz/o3tnLMefkUXjMfgNKHljPmq3PwT8zebXmBjRWsPvZlTK1TpqmLkPeL6R1qY/VfFlL5f28BUPPwYnKW/hDPqN23Q+w9vTVjI4OHhRCiF1i8LdIwnVVc2zAdKA1S9nlRw7wJ2dR9W9JmeYFV5Q1BDUDtkqLdbB2f8OKCJhVECK8s7nCZQrQkgY3o9ZRSG5RS53d1O4TYm86f5muY3jEupWE6Z3ImAy8ajeVz3u69g1JInpXfZnnJB/UnYXS6M+O2yDxndIfb6D9nP/A47XAPz8B3yOAOlyn2nGnl1dNJV5QQQvQCvz0xkWPGeKgKQGTNDuwD3Ewboxh0WC6eRA9jFs+l7tsSUg7Px5OT2GZ57jQfYxedSeV7W0gYlU7S5H4dbqP/xDH0++oHhFcVk3DEMFyZbbdD7E3SFSWEEN2SbRtW7IhQXG03W7610rCuzFBaa/PqyjDby8Js3B6iqsZupaT4VAcN3xYZAuGu/XxbVmWzsSBMoCrE9m/LmWjqOHagDTtsPIMgd2oW1YV1AEQy/Jj9+lGxqYai5WVEQs45sKtD1H1bTGhLBTWfbqN2RQnGGEJlAWq/2IF/eDKW14WprMV8uxVTG4zZlkhpHcHlxZhI83Nr6kLOfjUBALwJhsRDBuLKSsIYQ9XKcgI7nTaytgC2ttJNFgzBt5uhsjb2+hhMRbTNRRWwfAuEI7ClGNYXxl2G6HkkYyP6iiFKqXeBg4ANwPe11p8opR4Cwlrry+o3VEptAG7SWj8WnT8M+D0wASgF/gX8WWvdG7K2PV7ENpx6XxWvfhsi2Qevfj+VWaO83LvE5sp3bGwDrtogpqiW/YNBMmxDSpLFHT/JZuwwX9sVtLC+zHDYkxG2VsHEfrBgrpsM/77/5LtwWYBf/KcUX2WAYzdswIQMnlCEnKIyrJBNJMfFk9VvYCKGAQdks3PRTowNyRUhEgI2gdmDOOm3k9hy7HPYWyrwWBEsAyHcuI8aQd0nW+hXV4QFVJPAEP9GvHU1MKo/5uNfYeWmNbSl7rNtbDvuWezyAIlHDWHgG9/F8roxRZXYM38Hq3bA0GxcM4djPfEJeN2Yx65k8YtBCp7diMvv5rDjKkh69gNwWXDv5XDpUY0HW1kLs26Cr9ZD/wz46HYYM3C358cs2wJH/h52loE7CJEIDB8AG0rAGPjVGXDrWXvjV9NjyOBhIXq27wFXA+nA28DD8eyklJoAvAbcCeQAJwE/Ai7YO80U7fXF5givfhsCoDoId73vfPq/faET1ADYfi+Ztk1GdEFVjeGF96v3qL77v7HZWuVMLyuCZ1d1TXz7xNvVBEMworgUgjZYFklVtVjRTEyozMJEnLbtWOgENQDVqR68gQh1upjld35NeEsVbmys6GF4iVDz3may6koaLntJ1OGpq3Fm1uyAJz5p1payv36JXe5kZGrf20Tt/C0AmKcWOUENwMYiJ6gBCEWovvlNCp7d6MzXBp2gBsA2cPsLzQ923udOUAOwowzuebPtE/Tv92BnJRDCikQHVq8vcoIagN+96GRwRK8jgY3oK/6jtV6mtY4A9wGjlFLpcez3Q+AZrfVLWuuI1noF8A/gwr3Z2HqVlZUy3cZ0bopVPx4VgIHpLiorKxnYOH4WbEPAspoNjExNbLyotafegSnNP+VmuGp3u/3emk5Pdtpf6/VgRS/WtrvxRDQ9Wsvd2GaX7YysCHtcpAxKApoPGDWAcbkI426y1Gr+2X5gZrP2eAYmN65zWQRSo3sNzGi6FyahsZPA6p+IK8Fpr42LcIK/1fIZmNWsnLrspIbp1s5PILt+/E7Tljc50tx0Kmtr2iynO093lMGK+erpLGMkmy56txhdS8OA9cBg4HZ20xWllHoVOAoINCnSBWzWWk/cB82Xf9A4PLM4yN8/qmNEtou/npFEeqKLdWWGa963qQoaqirDrNwYYIovwpQEm+EDPfzwrDT8vvZ/tovYhl98ZPPpNsOckS6uP6hrPh+WV9nc/XQFO0vCHFKyg4plZbi9Lgb7I5RvLcA9wU2GGUCwMsS4s4ex7oVNVC4rw1cUwM7yM/SaCUw+ZyiFN35M9QebcVXVEd5Zi52TRr+bD6bof2vxvrMMt2UIDO1P/uBqEksKYfYkrF+f1qwtdnWQomveJ7iyhLTLJ5N2QeO/hv3beZg3lmIdPgbr6HFYt70I2Snwtwsp/KqadX9ehj8/iYnnZeG98wVIToC/XATDc5sf8F9fgWc/BTUS7rgQvLsfSWGCYfjpk/DFenCHsIwNx+0PC9dCIAx/PBemDe+k30aX6VAUssL6c8z3l3Hmuh4d3UhgI3q9NgKbG4B+Wuu50XUeoAJnDM5jSql/AUZrfVVXtB0JbMQemDdvHgBz5szp4paIvUwCmxhk8LDo674A7lBKDQe2AbcC3ibr/wV8qJR6A3gDJ9AYA+RorT/c140VojVVFRGefWwnleVhUnMSye4f/91Dom/qDd1OscgYG9HXPQ68DHwJrAU2AVvrV2qtlwInA9cA24FC4CGcgcRCdBtPPLCDhR9VsGxJDfqDfEIheXsXfZNkbESvp7Ue1mJ+A81TuJdFX/X+2WL7T4Gj91LzhOgUJTsbv/4gHHYTCkhgI3ZPMjZCCCG6rWNOzsQVvZFpwJAKEpPDu99B9HnylQpCCCG6LTUjlRGj/dRU23y55C2s3vlhXIg2ScZGCCF6iax+XgYNTZCgRsSltz7HRjI2QgghRB/UG4KYWCRjI4QQQoheQzI2QgghRB/UGwYKxyIZGyGEEEL0GpKxEUIIIfqg3jrGRgIbIYQQog/qrYGNdEUJIYQQoteQjI0QQgjRB8ngYSGEEEKIbk4yNkIIIUQf1FvH2EhgI4QQQvRJvTOwka4oIYQQQvQakrERQggh+iDpihJCCBFTWZ3huVWGvGQ4aeS+S4SHN1dQ8/o6fBP74Z85CIDadzeQ+WYJFQelxVeIMfDiZ1BWDWfNhGR/s9U135ZSMX87wfH9+MYkM2KQl/EjfbsUs2NFBTuWVzB4ehaZQ5KarQvVRVj1zg4Skj0MnZpGyXMb8A5IIuukIXt24ELshgQ2QgjRAcGI4fAnw3yz05n/7WGGG2e493q94YIqtqqHsAtrwILcZ0/HLqml+PLXGQQEn9mJfWoAV1rC7gu64VG480Vn+r/vwMe/A8v5JF/9dTHfHPwidm2EsNvFsyfPYEduBr+5KouDpiQ2FLHly1Ke//GX2BGDL8nNuQ8fRMZgJ7gxxvDC1V+y7etyLNugthRiba8GYMjvDmDQL/bv7FMj4iS3ewshhNjFhnIaghqAeWv3zeUi8Nk2J6gBMFDzyhpqXl7dsN5XGCLYtGGtefnzxulPV0JRRcNs2dtbsWsjAHgiNiO3FmEbWPRNoFkR6z8pwo44xx2sibDly9KGdXUVIbZ9XQ5AQiDUENQAlM7bFN/Bir3CYMV89XSSsRF7TCmVBTwJHAyEgSxgsNZ6S5c2LA5KqXuAsNb6R62sPxSYr7Xu+f/lYq8alAqDU2FzpTM/Y+C++ZPxTcnFSvZiqkMAJMzIxy6upXbeGgDCqW68Y7PaLuiQsbByqzM9Nh+yUhpWpR6cCy4LbIMBtuRmADB+RPOuqIGT0/kiOu3yWOSOb+wG86d6yRqWTMmGaoI+D3a6D1d5EICUGbntP3Ah2iCBjeiIK4AUIBsYBKzv2ubET2t9RVe3QfQOSV6L+ed6uO9rm7xkuGLqvkmEe4dlMOCj86h+diW+STmkzJ2AMQZ3bhLL3/icsiPSGdUvqe2C7vkBTBrqjLG58jhwN3ajpc3MY8JbJ1L+zlYqxuVwUGoW5w72cvgBzcsdeXguc+6Ywval5Qw/pB+5Y1Ib1lkui+/8cxpfP7+FhGQPYw84iOJHVuHNSyLvivGddj5E+/XWrigJbERHjACWa63DSqmubosQXWZousVth+39cTUtJUzLI2FaXsO8ZVmkfm8KO3La0cXj88K1c1pdnXF0PhlH5wMwaTfFjDw8h5GH58Rcl5ydwIzLRzbO33ZA/O0Top0ksBF7RCk1Dzg+On0O8FyL9VOAvwETATewEPiR1nqtUmoi8BWQr7XeGd3eAtYCt2itH1ZK/QS4EsgHSoHHgZu01pHo9ga4CrgEGAcsAy7WWq+Irk8Cfg+cASQCC4CrtdabousfwumKuiw6Pxr4LzAdWAc82OJ4zgF+g5OZqgHe0Fpf1LGzKDoiYhsuet3mudWGg/LgxdPcZPg73g305DdhfvhKiEQvPP4dH0cObztgeWalzRVv23hd8OiJLo4dtvusTVFZhF/8u5T120KcNDOJa89JB+B3C8L8bkGEQWkWL5zpYXyOi7pVZaw77XWCGyvpf/3+DPjNAWz593Iqr36DhHAAk+ilLuAm7PKSfOhA+l08hlE/XoNxW1T/bwPJs4cRfugzgtc8D8k+VgycwLZNFv2PzMP72VY8W0vwE8Q3sR8DXjod75Dmd1NVb6/hve9/QvGqClbl9iPPruO0rz7D5YLHZhxO4YSh5K4pICMS5IhFmsCgbJabdNxJHg7760EEM5N4/M6N1FZHOOmC/hz06BPw4iKYMQZevB7S4sgqtSG0aAvlZz2NKakl5c7ZJP6gSeC0owxO+T18sxEuPAL+/YOGwdF9nd0LxtPEIoOHxR7RWs/BCTYe1lqn4Fz0mzLAzTiByTCgCngsuu8yYDFwXpPtjwD6Ac9E57cAJwBpwKnA94DLWtRxMfCd6H6bgb83WXc3ztifg4GhQBEwTym1y1VKKeUBXsEJjnKB7+J0s9WvTwIeBa7SWqfiZKrui3FaxD70/GrD48sNdWH4cAv85Qu7w2VGbMP3XgxRVgfbK+EH80Jt7mOM4ZI3bErqYEcNfP+tttvx0GtVrNgYIhCC5z+oYfHqAOtKDb98P0J1CFYWG65/1xm0u/Xnn1K3vBS7Jsz2mz+n5usiCn78HonhOlwY3LVBZwxM2FD1wVa2fv9d3JU2nrIIO77/NqY2SPDyJ6G8FraVk//lEuy6CNtf30rd5gr8oToI2QQXF1Jy88e7tHXJ35ZTsrQMK2gzdkshp+qFJNXW4a+u45z5H7KtDLZ7kygNuvliwDC+Lk8kWBmmdkcdn930JS/du5WynSECNTbrf/M2PPUx1IXg/WXwt9fb/TuKpfLHr2JvLMNUBqi86lXsstrGlb97Dhathtog/OctePfrTqmzN+itg4clsBF7hdb6a631+1rrgNa6HLgFODgaJICTEbmkyS6XAP/TWtdE939Oa71ea2201l/hBBZHt6jmTq31Jq11AHgIUABKKRdwEU6GZ6vWuhq4BhgPHBijuQfhBF8/01rXaq1XA3e12CYEjFNKZWmtq7XW89t9UvZAZWWlTO9muqlAMNhJZTaOPLBtu83tKyurYranve2vqo5dTjgcjrk8Hsa0L9gLhRoDudba2V6RSGMbWhvTsTf+HlpbXlNTG3ObnjgtYpPARuwVSqmRSqnnlVJblVIVQP1HwfpO+CeBMUqpaUqpVJzMywNN9p+rlPpcKVWslCrH6XZq2YG/vcl0NVA/YjEHSKDJYGatdRVQCAyO0dxBQGF9UBXVdN8a4EScrre1SqkvlFLntn0WOi41NVWmW5k+Y7TFeeMtEj0waxDcMMO/R+U0nXa7LB44zUdmIgxIhf+emtDmvmlpqTx4vIssP+Qlw72zXW3WdfGJKYwb6sXvszjjiCSmjk5g8qBUfnukm2QvjM22uONoJ7k49E+H4Z+QiSvZw4CbDyBpcj/y/nEUtR4/NhaRJB+4LCyPRcoR+eTfezSRVBfhTDd5/z0OK9GH779zIT0RBqazdfoU3IluBpyQj39wGnVeP/jc+Kbm0v+3R+zS5ilXjydrvwyMz8XKwbm8qGZQk+inLtnP/w6fxcAMGBCqIdMXYfr2DUxOr8WX6iEpL5GDbp/GGVcOJjPXS0KSixG3HAvnzAS/F46cCFef0KHfV/106t9PwjU0Ays1gdR/noQrI7Fxmxu/AweNhkQf/GA2SXMO6lBd3Wm6o0wrr55OxtiIveUeYBswWWtdrJTaD/iG6Leuaa3LlFIv4nQnLQE2aa0/BVBKDcbptjoDeF1rHVRK/YloRiYOO4EAThZmTbTMFJxups0xtt8K5CqlkpoEN8OabqC1/gD4INqVdQrwnFLqM6312jjbJDqZ22Xx2EmdP2B37iQPcye1763xzLEuzhwb/+fEfhlu/vuLfrssv/FQDzce2rxu/5gMJiyb22zZoCvGw27uKJqftQ6ACbOHAeC5+CA8FzsX9Glxt9KRPCCJOfOOabH0WABuaJgfFv15OABTWmz98/9OaJw58Zp2tqBt3gMH0W/DdbFX9s+AhX/s9DpF9yWBjdhb0oDVQJlSqh9wa4xtHgSewOkKajpYNwUnm7gTCCmlDgYuAJbHU7HW2lZKPQLcppT6FijD6VpaASyKsctCYCPwR6XU9cBAoOFdUinVHzgUeEdrXa6UKouuisTTHiGE6I56w3iaWKQrSuwt1wKHARXAfJzBuS29g3OH0XTgkfqFWuvlOIORX8IJSn6O03XV3vo18DmwCRgAnFJ/V1VTWuswThZmMk531fPAvU02ceF0hW1QSlUC/wQu0lpvaGebhBCi2+itg4ctY3pDj5oQvZb8g4p2mzdvHgBz5rT+fBrRK3QoCllg3Rfz/eVQc1mPjm6kK0oIIYTog3rrpybpihJCCCFEryEZGyGEEKIP6g3jaWKRwEYIIYTog3prYCNdUUIIIYToNSRjI4QQQvRBMnhYCCGEEKKbk4yNEEII0Qf11jE2EtgIIYQQfZB0RQkhhBBCdHOSsRFCCCH6IOmKEkIIIUSv0VsDG+mKEkIIIUSvIRkbIYQQog+yu7oBe4lkbIQQoq8JR7q6BULsNRLYCCFEX7GtBCZdA76zYO6fwe6tn9lFPIzLivnqapZlHWtZ1v2WZc2LzivLso6Kd38JbIQQoq+44wVYugmMgacWwGtfdnWLRBcyVuxXV7Is68fAv4HVwOHRxbXA7fGWIYGNEEL0FQneFvMyzFJ0O9cAxxhj/kDjMKAVwNh4C5C/aiGE2IeK520kXBKg33eH405uEWis2U7J44spS8ii/7kT8eUksur9QvxpXkYe2q9D9YbDhkVHn4j32wiTPv+Yb045hYS0EUwyBsvq+u4Hse91h26nGFKBzdHp+ocje4FgvAVIYCNEG5RSRwDvaK3l/0V0yIZff8Hm274CYPu/lzPlk1Ow6i8uq7ZRcOAdLEg4EGOV47tvEzUzBrF9bS0AB100lJk/GLnHdf/1LwUsWVILGbPIOWMWkR21cMdmjjwlm1MvyuvwsQnRST4Cfg78tsmyq4H34y1AuqKEEGIfKX5hQ8N05Wc7CW6vaVz53jdst7MxlvO2HKyKUL64tGH1mo+K9rjecNg4QU1UUUXjtwR9/VnFHpcrejbjiv3qYj8GTrcsawOQalnWSuAs4Lp4C+j6QxBCiD4i7eDchmn/iFS8uYmNK9VIsiIlDbMuj0XCkJSG+byJaXtcr8djMXSor2E+uXGSYWOS9rhc0bMZtxXz1aVtMmY7cABwNnAucBFwoDGmIN4yJLUu+hSl1E+AK4F8oBR4HLgJZ5Da7cAlOH28xcBdWuu/N9n3bOB3QD/gTeBSrXXlPj0A0ej+d+GhD2DiYLj7IkhM2OtVRtaVUHnN65iaECm/OwbvgYNa3bb22WXU/O0z3MMySLxxFjt+tRBrfTm5gy3wuMi5bSqbzn6dSFmQ/jcfSMrho8h/5kJGXzefgopkIgNymHblcL55aiOBLVXk+CIYY1j8WiHfvL2TnGFJHPvDYXh8jZ9PiwuCPHTnRnZsHUxG/zom5ZXw+cMb2On1E0zPICPLjbc6iCccITXfx/RD0jnmjMaxO/96u4Z3lgapsaHODadMTuC6oxL5z7s1vPl1kBKXi80BFxEsjhjh5uuCCOtKDIeO9vH0d70keXe9KBZU2lz9SpDCasMvj/Bx7Ch3m+d50XbDjfNtEj3wl6NcjMzYs4vtextsbvvEJjsR/naMm4GpnXfR/miz4eZPbDL88NcjXQxO65bjVXokY4wBPou+2k0CG9HXbAFOADYAU4E3otPrcT4ZHKS13qyUysUJfuq5gdnAFCAZWIDT79u0H1jsK4vXw+X/cW5bXrACslPgt+fu9WrLz3uW8MItAJSd/Dj9Cn6G5do18R3eWEbZ3OcgbMP8TVR9uJWKTTYu7IZv59l66TvYdc5NHzVfFDJxx6V8tRDWlefiMsDmGhb9eCF10T2++ecKTLKPN54rBmDz0kqSMjzMunhIQ70P/nEj2zfWAW6KNyfz8C1r8ZdVo0dlYZeHAQt/yE1mIEx1TZht24J4o4HRO98E+OO8xq6xcgs+3RCBkOGvr9ZQ5bYo8Vngcdrz8FcRiNhgDK8uqeOmXBd/PnrXS8oVLwV5abnzQEC9tY6tNySR7m89CLCN4eTnI+yM9pztrI2w8Lz2X6qqgoZTn49QFR1yWhuO8OqZnXPJqwsb5rwQoSJadmXQ5u0z2w7Yuhu7Gw4etixrM42DhpsxxgyJtbwl6YoSfYrW+jmt9XqttdFafwU8ChyNM+LeD0xUSvm11oXR9U39XGtdpbXeAbwIqL3d3srKSpmONb29zAlq6m0r3Sf12tsa501RDQQjMbe3d1Y7QU39tmWN41salgUan/5rVwSpKCildkfz7SKh5g/QK1lX1my+sjjUrN7KslCz9cEIhN0u7CbBV6TJxay8NNyw746K5nW5o6d3Y5HTzggWtPzSxPpZA+tKGutuek42l4UbpquDUF5ndtmm6XRJeRVFTU7Dtqpdt4lnentJVUNQA7Ctcvf1tme6OkRDUAOwpSIS976dOd1LnQ9c0OR1PbAVuCveAiSwEX2KUmquUupzpVSxUqocuArI0Vp/ANyI0y1VqJR6SynVNHCJaK13Npmvxumy2qtSU1NlOtb0UfvBoeOcBVkpcPWJ+6Te5JtmQTQwSLp+JpbfG3N777QBJMwZA4CV7CX5ukOwfO5m36acceYo8Dhvwdk/nETGsBzGXDwaO9nT8PCO4ScPJnVoMgApg5M5+CcTGTTRqSMx1cMBp+U1q3f22Y1jeCyXjZqRSmIoTE6FczF0YUivDQDgdsOxp2Y37HvS1ARG9Y9mHSyosWDiADc/PSGRcQPdJEdsvMYG2wkQkn00BJe+JDc3HeaPed5+eaQfX7TYS6Z5GJLh2u157peRyg0HOufJZcGvZux++9amR+elcvkUpxyvC248xL1H5cSazk60uGqqU7bHBb86xBP3vp053VHdcfCwMebDFq+ngNNxhgnExTImZsZHiF5HKTUYp9vpDOB1rXVQKfUnQGmtj2iyXRJwM3CO1npIrNu9lVI3A4dqrY/Zy82Wf9DWhCOwejsMzIT05H1WbWRrBQTCuEdk7XY7Y9tEVhXjyknGlZ1EeEc1kYogLr8HbIN3aBqhgmrsqhAJozII1YR5/YL57FxSij/TxxF/UuTPyiNcF6FqczUpg5Px+N3YEUPJllpSsn34U3btWiktDPLS8x+S1i/IGd89idItNXj9btZvCfHMnzcSKAuROyyR7/1qOJk5vmb71oUMm4oiZKS4KK62GdHPTYLHIhAybCyKkJniYnuljY3FiCwXlQGbbwphxhAX6Qmtd2sUVhnK6gxj+sV/1VxXZkhwQ34Hx8WsLjGkJUD/5M7vdllTakjxQd5eKDtOHar4pcwnYr6/nFp6brfqo7IsKxPYYIxJj2d7GWMj+pIUnCzlTiCklDoYJ9W5XCl1IJAALAICQCUg3xTYnXncML71wbt7izs/vruTLJcLz7ichnlP/2Q8/ZsHYN68xvlN721n5xLn9u660iCbFxSSPysPj99NxujGOl1ui35DW7+TKTPXR3Z+XeP8IGfbtc/tJFAeBsuicGMdW9fU7BLY+L0WYwY4l4Xc1MYgJKHJ8pwmy9P8bvLjuNTkpljkprTvWjliDwcMtzQ6a+9do0dldqvrf69gWdatLRYlAScCr8dbhnRFiT5Da70c+A3wElCG8xCoJ6OrU4C/AkU4d0TNxrndUIh9IjHb33y+X+fe5ZWS4d3tvOh7uuN3RQGDW7z8wJ9xbu6Ii3RFCdG9yT9oH/LNfatY//pW+u2XyUG/nIzbt2efPefNmwfAnDlzGpaFgjbz7tvGtnW1TJ2VwaFzclrbXfQcHQpDXsyK3RV1Wkn36opqL+mKEkKIbmLSZWOYdNmYvVK21+fijB/u+6470X11l++KsizrqHi2M8a8F892EtgIIYQQfZDdPeIagPvj2MYAI+IpTAIbIYQQQnQZY8zwzixPAhshhBCiD+ouXVGdTQIbIYQQQnQLlmWl4TxHbBbO9/I1RF/ylQpCCCGEaFU3vd37X8A04FYgC/gxsAm4O94CJGMjhBBC9EHG6vooJobZwHhjTLFlWRFjzEuWZWlgHnEGN5KxEUIIIUR34QLKo9NVlmWlA9uBUfEWIBkbIYQQog/qRrd7N7UEZ3zNu8B8nK6pKmBVvAVIxkYIIYQQ3cXlOF9WDPAToBbIAC6MtwDJ2AghhBB9UDe93XujMSYCYIwpBC5rbwGSsRFCCCH6oG56V1SBZVn/sizr0D0tQAIbIYQQQnQXs3HG1DxhWdZ6y7J+b1nWpPYUIIGNEEII0QcZy4r56tI2GfOVMeb66MP4LgYygfcsy/o63jJkjI0QQgjRB3XTu6KaWgEsx3lA3+h4d5KMjRBC9DFPLAkz7V+1nPZ4gMIq09XNEaKBZVkZlmVdalnWu8A64Ajgj0BuvGVIxkYIIfqQzeU2Fz4fJGLDV9sjpCYEefS7CV3dLNEFurrbqRXbgE+AJ4DvGGPK2luABDZCCNGHlNVCxG6cL6qRjI3oVkYaY7Z3pADpihJCiA6qqrFZtylIMLTvgwS7JkTN4iIiFcFWtynZXEPZtloC1WFyKqu5YAIkBkOMrankFzPivwzYFXWEFm/H1LRel+g5uuPt3h0NakAyNkII0SGbtoX4+R07qaiyGZbv5Y835JCctG8+M4aL61hxyPMEVpXjyUti3ILTSBiZ3mybj+7fwKePbgbAl+QmWBNhZj8Ps7aVE6oIUbAulcAjM0lI9e6+rvWllMy8H3t7Je7R2WR/fCmunOS9dmxi77O7Z1dUh0nGpodSSm1QSp3fCeUMU0oZpdSg6Px5Sqkl7di/Xdt3NqXU8UqpNUqpSqXUdXupjpuVUu/sjbJFz/fmR9VUVDl9Oxu2hvj8m7p9Vnfpc+sIrHK+LzBcUEPRQyubrTfG8NkTWxrmgzURAOo2VBKqCAFQsrqSjR/uaLOu2oe+wt5eCUBkdTF1z33bKccgRGeTjI1oRmv9OPD4nm6vlHoICGut2/0Y7D30N+DPWut/7aP6hGimX5a7+Xymu5UtO59vcHKL+ZRm85ZlkdLPR8WOQLPltrv5J/WUvMQ263IPbp4JcrWYFz1PV3c77S0S2IiebgQQ94ObhGgqHDE8+341hSU2cw5NZPhApzvGNoZ7FhtWlRounOgiy2v4x8cBXBaEDPRLdnHdYT4SvRYnHZnMM2sM3xTaZKdZXLfAxv9GOXMHRZh1YDLz368gI9PD0cdn4PFYLHt+MyVrqxh93ADyJme02rZX1tq8vdEwK9dm7LurCZUH6T81i4r3CkjZL4P8y8aQfsJQsq6dSuEzG0melEn2peMof+Ab8h/ZQmK4huC6jzj98qF8+Lc1uBM9mIwkKjdXk3tgP+o2VBKujTDpvOEkjc/kLy9XgwUXH5VIRrKTzK/9ZCvFf/icUE2EjB/tj+/UcYS/2EbCnDEk7J+Duf5JSPHD/52AleyP/8RX1sJdL0FtEK6dA3mZHfk1xu+lRfDeN3D0JDjlwH1Tp2gXy7IsnO+Hmgv0M8ZMtizrcCDPGPN0PGVIYNONKaV+AlwJ5AOlOJmRm7TWkRjbTgbuAKYDbuBLrfUx0XUPAsfgfEPqZuB2rfUTrdR5cbSOUdH5D4AvgGE4j7ouBK7TWr/Ucnul1PXAedHl50SLHApsAQ7RWn/VpJ6PgLe11re1cQ5mRY9rHLAduFtr/R+l1ECcr7F3A28ppWxgmta61a+23915UEplAvcCR+H8X2wBrtBaz4/ubimlfkfjF7L9W2v9m921XXR/975YyZNvVwPw1qJanrw1h9QkF39cZLhxvtO99MDSCGlVtWwttcECouMSVhTaPHpOIrd+EObJrW7ADcVAyAMuLx8tDXDey5sJBJ0BxWWlYcZ5a5h/x3IAlr2wmblPH0r6oKRd2vXBJptTXrAxQOS1zwmucMbIrIoYMgsDWIAdtMk5eRAb7l1HpDpM5ZYCks55k8izS0kFwFD98TySk92cWB1kZVY+esAYALZsqmyoy3JbXPWfcr5cFwbgi7UhHr0mg+CaUrYc+T8IOm83O99dRxpVANTe9yWJry3Au3GbU8g3m+GZq+M/8efeDa9oZ/r1r+Dru+Pfd0+9vRhO+4Mz/bdX4Z2b4ejJe7/ebqyb3u59K3As8BfgnuiyLcDdQFyBjYyx6d62ACcAacCpwPeI8U2nSqkBwIfR1zAgD/hDk00WAFNxLui3Ag8ppSa0ox0XAXcB6cA/gIeVUru8G2ut78AJvh7WWqdEX8XAM03brZQaA8wAHthdpUqp4cAbwL+BbJzHa/9eKXWm1nqb1ro+7z47WlerQU3U7s7Dz4AknEAsAzgd5/zXOxzn6ZcDgVOAG5VSM9uor8MqKytlei9Or9wUapgvr7LZURKhsrISXdB4d1NlELZWRWeaXAj0FueCv3BjizuEbGdfC6shqAHYsL6Owm/LG+bDdTZbl+2M2bZPNgWo33P4jtLGot0WJvquXfJpAdXLy4lUhxvWB75qOlbGIowXqp32FSemEsu2xTtZuqmxjGXRc1L++eaGoAbAS5PjDNmEN1Y0zuv17Tv/ek3jvt9spLKoJP5993T6i3U088XabvN3uKfTHdUdv1IB533+ZGPMU9Dwb7AeJzsfFwlsujGt9XNa6/VaaxPNdjwKHB1j0wuANVrr32utq7XWQa31O03KuV9rXay1jmitn8LpujmiHU35n9b6E621jZPVSKcdj7eO7nOuUqo+V30p8IbWemsb+83FyTw9pLUOa60XAv9hD77GHto8D0Gc4GksYGmtV2mt1zfZfZXW+p4m7VgMqD1pR3ukpqbK9F6cPmJaY/fJiHwPg/t7SE1N5btjLOrf3kekw8H50TnTGKicOdlJeM+d2uLhdtHxK/5wmOzsxvE20w9MZdTReVjRd92U/n6GHzggZttOG+cnJXqT0hfjBjUs9wGW7WRZBp49krQD++EfGh1n47JIPXtcQ/0uIngJYg3OAGBIxc6GuKy+DZbbYszxgzlu/8ZjqJ/OPGYUrlzn84sB6rzJ4HF2tPol4Zs1tPGYzzywfef/zEMa9z3lAFL7ZcW/755OnzQdkqLHmeyHE6Z1m7/DPZ3updxA/UeJ+n+4lCbL2iRdUd2YUmoucB1OpOrBeV9bGGPTYTjdMrHKcAE3A2fjZHIMkAzktKMpDc8V0FpXK6UA4v7v0lovUEptA76rlHoKJwP0/Th2HYwTqTe1Fid71S5xnIc7AS/wMDBAKfUKcL3Wuv4jcMtnK1TTjnMguqfTZyUzfKCHnaU2h0xKIMHrXPnnjncxIsNiTanh+OEWSZ5kXloWItUHVSGLDL/FcWOdt89LlZdxOS6+3WGDbVMRsAkUBTltrJsRI4ex5MsqMrI8jB3vBAlnPjKD0vXVDD44m8RMX8x2TehnsfgiN59sNRz0vcmkfp1LqDxEv6lZlM/fQfKEDNKmZQNw4OcnU/zmVpLGppN+QD/qvjucz+97g4RwHUMvPBv35AGE5y1j9MB0rPvWsOOdrYRCCVT7fAyaM4ghh/XnDzMNx05NwAKOmuS0yZOTxNAlF1H++ArCNRHSjhtM+IfPEF5WSMLxQ3Dd91146UtI9WOdMKV9J/6vl8LsqVAXhFP30ViXSUNh8V2wcBXMGAujBrS9Ty/XTQcPvw782bKsa6FhzM1twLx4C5DApptSSg0GHgPOAF7XWgeVUn8idpZgA/DdVoqai5PhmA18q7W2lVIa2Ft/0nYry/+Dk6mpAiLAq3GUtRk4scWyEdHl7bXb86C1rgZ+CfxSKZWHc+7vBC7cg7pEDzJ1dOyvEzhogMVBAxr/Tc6ZGjsIAZg51M3MoU3vhmos86CZac22zZ2QTu6Etu8oGplhMTIjWv8RjRfhpKHN73zy5fgZcP7Ihnn//v0pO97JgHhmDgfAe+50ql9bh/vxb0gghRAekqml9L+rKbtwJBmH9ufYKbueB09eCtn/57zlBG6YB19sxANEHvsc+9ypeM46qM3jiMmy4OS9nvDc1eiBzkt0Z9cCDwHlOB82q4C3aMd7sQQ23VcKTlfhTiCklDoYp8tpeYxtH8O5IN8A/B0IA4dHu6PSovM7AVd0sO8U4JW91O4C4GCllCvadVXvUeD3wG+AB2MNgI7hSeBXSqkLcb43ZBrwA5wB1e212/OglJoDrMHJfFUBdTgBmBC9gmn4HgWrxfL4npa8y3aR1j7DiJ7CuLpXysayLDfOh/Rzcd6zhwKbjTEF7SlHxth0U1rr5ThBwEtAGfBznAt9rG234YwVORZnwGsBzmBYcLpWPsO5aG8FJgDzdy2l09yH08VTrJQqU0q5o20sBZ7FCSbuj6eg6BiXE4Ef4dxv8ijwK611XCPjW2jrPIzESXVW4GTAaoEb9qAeIbql5JNGknreeNLdtfgSwfJY5P9gDJmz8uLa3/ezI3GpweBx4blA4T5x/F5usdjbutvgYWNMBPizMabOGFNojPm8vUENgGVMfNG6EB2llLoZ57bv2V3dlh5E/kFFu82b5wxHmDNnzi7rjDFYltXws732dD+xV3ToF/Gf8S/FfH/5wfJTu+wXbFnWo8DTxpi4x9S0JF1RYp9QSvUHLie+QcNCiL2kPijZ0+BEgpreo7t1RUX5gWcty/oUZzxlQ/BljIlrnI0ENmKvU0r9GWdszKNa61dbrGvtFr75WusT2lnPMpw+2ZY2aq0ntqcsIYQQXWJp9LXHJLARe53W+jqc29ZjrUuJtXwP65HgRQgh4tUNs2/GmFs6WoYENkIIIUQf1B27oizLOqq1dcaY9+IpQwIbIYQQQnQXLe+azcF5OO0W4vxaBQlshBBCiD6oG3wv1C6MMcObzkefbXMTEPeXZMlzbIQQQgjRLUWfbfNb4Pp495GMjRBCCNEHGavH5DaOpfWv69mFBDZCCCFEH9RNBw83e3YNkITzbJur4i1DAhshhBBCdBfnt5ivBlYZYyriLUACGyGEEKIP6o6Dh4EDjDF/arnQsqzrjDF/jqeAHtPBJoQQQohe79etLL8p3gIkYyOEEEL0Rd0oYdPkwXxuy7KOpHnrRtCO270lsBFCCCH6oG7WFVX/YD4/8ECT5QYoAH4cb0ES2AghhBCiS9U/mM+yrEfi/Rbv1khgI4QQQvRB3fF2744GNSCBjRBCdJixbUI/e4nIB6txHzce729Pxupeaf64GGP46Uu1PPVZHa6IYUCSxX79XdRV2yQlWNx4Ziphn5ur3opQE4Y/zHJx2GC5B0V0Hsuy0oCbgVlAP5qMtTHGDImnDAlshBCigyIPfkb4z+8DEP5yC64p+XjOntbFrWq/Z5aE+PcHtfiij0errLMpLwrji67/xcMVbBiQxqLtzganPh+h6GoLVw8M4kS3G2NT71/AIOBW4DGc59r8DHgu3gIksBFCiA4yhZW7ne8pCittrKbPfLWsZo+ALamyKaxpXFJWB8EI+OVK0iN108BmNjDeGFNsWVbEGPOSZVkamAfcHU8BkkMUQogO8lxyMNboHACsiQPwnKe6uEV75txpPvL7uRuCGY8x+H3Oxc/jgh+dlMyth7rxRK8cv57pwu/plhdH0XO5gPLodJVlWenAdmBUvAVInC26lFJqGLAeGKy13qKUOg+4Xms9pQvbVAUcq7X+tJX19wEerfXF+7Rhotuy8tLwL/0FZms51qAMLK+7q5u0R7KSXSy7MZ1VhRF8LoPfY9Ev1U1VrY3XbZGR4kQ0J460CEZgQIoENT1ZN83YLMEZX/MuMB+na6oKWBVvARLYiG5Fa/048HgXtyGlK+sX3djXG2DJBjhyPxjUr2GxXVpL8LWVuIdn4h2evctupqAM3lkGE/Kxpg0DoHxbLVuXlJGc6qZqczWBCORNycSX4KJ4ZQX5B2aT0j+xoYydqyspWl3JoOlZpPb3N69g/rewaSecrCA9GYCqFS7mLV9FZHAGE6alUuzzMP/TSg7PgyFT0vh8bZjxgzyMGehcBtYVR3hgUZAkD+Rjs3p7mIjLYtwQDyS6yU93ccwYF8Gw4d0VQVISLAZMcEbffLzVsKHccOIIi0y/c7FcWWJ4b4NNTXWYQwa7mTFs95ebV9faVIfgtNEWPnfrF9y310YorjGcOs5NordbXph7jG4a2FxO44DhnwC/AzKAuO+WksBGCCHi8f43MPtWCEegXxp89ScY1A9THaR8xj1EVhYBkPLgd/Bf3Dhw2OysgOm/hm2l4HZhXryGsiljefJ7nxMqC5BYXk1tejLG7WRDvOEwViiCP9PHWf87lJT+iWzWJbx49ZfYEUNippdzHzmYlNxocPOfN+GK/zjT+w2BRX+k7AOLqrcNZayiKtHPXyaO4aOsNIzlJiMYZOiLxQRsF1433PejdDLS3Uy5q4K6EFjGMCkQIsUYai2LFxZarE7wYlsWvz/Rz/urgry1MgzA9Uf5GTfMz/fetAEYlwX6fDerSmHmExFqw4DtguJqHj3Lz/kqIeap/en7Ee763Cnj+OEWr58Z+9J02wchfv2+U/fMIRE+usSHqxvesiz2nDFmXZPpQuCy9pYhgY1oN6XUBuA+4GjgAJyupPOAicBtQA7wDHCF1jqslHoQOAYn6t4M3K61fqKVsi8GbtJaj4rOe3FGxF8EDAQKgRu01s+20cbd1qmUmgzcAUwH3MCXWutjousMcJjWekF0/nvAL6PH9RLOp4lwPOdK9CIvLnKCGoCiCvhgGZw/i/DXBQ1BDUDg2aXNAhs+XuUENQARG17QbKzrR6AqjC8YxrjdDUENQNiy8AJ1pUG2fl7M2JMHsfaDQuyIM/KltjTElq9KGXfcAGeHZ5r0mC7dBCu2Uv21BdGRMim1dZRbjZ/Oy3w+UgMhkjGEIvDe10GScz3UhZwijGVR4naREo7gNQYvFn5jqLEsnlocZMnmxj/9ZxYHGRdsDFZWlMDSYnhrvXGCGgCXBT43z34dajWweWal3TD9xnpDVdCQ4ts1YHl6WaTxtG6y2V4F+WkxixRx6I4ZG8t5TsJlwFygnzFmsmVZhwN5xpin4ylDBg+LPXUR8EMgE6dP9AXgSGAKMAk4BTg7uu0CYCpOkHEr8JBSakKc9dyOc7vfmUAaTt9rPH2trdaplBoAfBh9DQPygD/EKkQpdRjwT+AKIAt4u8lx7XWVlZUy3U2mayfmN0wbrxumDHOW5yVgZTR2DXlUfvN99xuM8Xsb1tftN5DcsWlYLoh43FgRG+zGO41c0WmXx6Lf2DQqKyvJHd949XZ5LPqNSm0sX41sWEd2KpXZSSQMalwU9HjwNbmAeSM2frsxkBiZG2baIE+zL+ZJibYhYoENBKP7HzjYw6gmPW1TBxhU/8b5TD+MTIcJ6bWNC42BsI0a7G713E7NbgxYxmTYDUFNy+3VwMZL1pB0i5yk7vG30VXTvdStwKXAvUD9c2u2ADfEW4BljGl7KyGaiGZs/qm1vjM6fyLwKpCrtd4ZXfY0sFVrfW2M/TXwgNb6XzEGD19MNGOjlLJwvvjsbK31qx1sc9M6rwfO1Fof0Mq2DRkbpdR/Ab/W+oIm6z8GVu+jwcPyD9qdPPYh6DVw2kFwxH4Ni8NfF1D34Be4R2Thv+ogLFfzz4zmk1Xw9CKYPBjre7MA2PBpMesXFpFg29Rsr6HWthhySA4JCVC0vILhR/Qn/8DGcTzLX9tG4YpKRs7KYdD0rMbCIxH4+2uwqQguPRomDuHlF+ZR9I6HcHk2FYOz2e/wbFZ4fSxYVMWxGUHGH5LNgjURJg31MOcAJyh7ZVmQOz4I4AdGmzAbC8MEXBZDB/lwpbkZ2c/NNYcnUFRl+OtHdaT6La6b5cfvhX8uNqwvN1yyn4vJOU5Q8sIqm2eW29RUhTh2qMWVhyS02m1UHTTc9blNVQiume5iYGrs7epChrs/DVNcCz860M2wzD7/2bxDKZc7D/sw5vvLz+bP6rJUjmVZm4H9jTFFlmWVGmMyo1mcEmNMZjxlSFeU2FPbm0zXAJH6oKbJslSllAvnKZJn42RGDJCM063TlpzotnGPhgeIo85h7ShzEKBbLFvfnvaIXuT8Wc6rBc/kPFLuPqnV3axDxsAhY5otGzYjm2Ezdh1oDMCcXReNP3Eg40+Msa3bDdc038HyQM7xYebMObBh2fHANcckNcwfPrV5MSdP9HHyRB9tyc+wuOOUpGbLrp6263Xw9DEuTh/jIp7LTLLP4tcz276TzO+1+MXh3ja3E/Hpjl+pgDM0oCo6XR94pTRZ1qY+H+6KvW4uTn/pd4BMrXUGTtdVPP9RO3ECpNGdXOeGdpS5FScQaqrlvBC91uNfhci7rYqxd1aht0SoCBiOfzZC1j/CXP5mBFuy/qJzvQb82bKsBGgYc3MbzgP64iIZG7G3peEMtN0JuKJdTVOAV9raUWttlFL/Au5QSm0ClgH5QJbW+usO1PkY8Eul1A3A36PbHq61fidGWY8CbyilHsIZk3MOcBCwpq32C9HT1QQNlzxTRygCO6rgiufrmKMSeXODE8zc943hxBGG00d3y0/+og3dcfAwcB3wMM5D+rw4mZq3aMft3pKxEXvbw8BnOIHAVmACzkOX4vVL4GngRZzxNh/Q9hMod1un1nobcARwLM6gtAKcO692obX+EPgxzl1gJTgZ/f+1o/1C9Fi2cW7kqheyIWQ3z9CEIgjRYZZl5QEYYyqMMafjDBw+GBhpjDndGBP3qGkZPCxE9yb/oKLd5s1zsvZz5sQYrNNO//o0yHWvBEhNsHj2fD8T89yc9HwEvQNOG2XxvzkuPN1zrEZf0KET/4cjF8R8f/n5+4fu81+oZVkVxpi0JvPPG2PO2JOypCtKCCFEq344w8eVB3uxmnRbfHa+B9sY+VbvHq6bdUW1bMwRe1qQBDaiR1JKLQOGxli1UWs9cV+3R4jezIpxAZSgRnSyTstOS2AjeiQJXoQQomO6WcbGY1nWkTRmblrOY4x5L66C9kLjhBBCCCHaoxB4oMl8cYt5A4yIpyAJbIQQQog+qDtlbIwxwzqrLAlshBBCiD6oOwU2nUmeYyOEEEKIXkMyNkIIIUQfZHpnwkYyNkIIIYToPSRjI4QQQvRBvXWMjQQ2QgghRB/UWwMb6YoSQgghRK8hGRshhBCiD7IlYyOEEEII0b1JxkYIIYTog8wuX6jdO0hgI4QQ3ZAJ22y56xvq1leSd9lYUlXOHpUTDkT46oG11BYH2G/uMLJGpnZqOwMVIb68fw3hgM3Ui0eQmpcIgAmGqblzAZHNFSRecQDeqQM6tV7Rcb118LAENkII0Q1t/PUXbP79EgB2PrkWtfosfLmJ7S5nwR+Wsfy5TQCse7eA8149Em9S5731v33jV2yavxOAzZ/u5NyXjgCg6udvU3v3JwAEnl5K9tprcWW2v/1CtJeMsRFCiG6oanFxw3SkIkTd+so9Kqd4ZXnDdG1xgJriQIfb1qz8FRUN02UbqgkHIgCEF29vWG5Ka4lsKuvUekXHGcuK+erpJLARQohuKPe8UdQPgUialEny5Kw9Kmf0SYMapvP2zyJ1YFJnNK/BmJPyG6ZHHJOHJ8ENgP+8KQ3LPdMH4hm3Z11pQrRXp3dFKaUGAZuB4VrrDZ1d/t6ilHoHWKC1vrmr29IWpVQW8CRwMLBGaz29nfuHgWO01h8opc4DrtdaT4muewgIa60v6+Rmt9Wm+wCP1vriTixzGXCr1vp/nVWmEPtK7nmjSByXQWBjJRnH5uNO3LO368nnDSd3vwxqiwMMnpmDy925n8hnXDuewYfkEK6LMOTQ3IbliZdOxzO5P/aWCnyzR2ElyMiH7qY3ZGdi2et/aUqpC4ErgPFABPgc50L6TYttbgCGACXAg8AtWmsTXZ8L3AMcC9QBDwC/0FrbLepaA5yntf6sg23OAl4ExgF+YGe0Tb9t0qargfOAScA2rfWoFmVcHG1nTZPF87TWczvStqgrgBQgW2sd7khBWuvHgcf3dP+uCoTiobWe2NVtED1bRcCwpRJGZ4I3GhAEI4Y1pZDiM1SHLMZkgtvVeIGoCho2lRtGZlokeOK7cIQjhtXFhoFpFun+xn0S0twkHpKLK8FNzbelJAxOpi5gCNWGSc/yYNbspKYMTJKXpNFphD7ehDvdR0JxHRiw1xfjGp5N/1HJRLxB3MYGnIwKW4ogFIHh/Rvqi6wtpsbtYZMnmYGllaSNTMUEIoQKajDD01i3I0x2QSmlkQQGT0ghNcMLQP+xqQS312Jsm9o1lVjhMNVrS0nM8JA4JgUrGIRkX+MBl1fDtlIYlQdeCXi6Sm/9Esx98ReVCvwG+AQIA78G3lJKjdRa1yilpuAEAGcA84CxwPvAVuC/0TIeByqBQUA28AZOAPTH+kqUUpOARGBRJ7S5GrgSWKW1DimlhgOvAYXAvdFttgF34AQ/l7RSzrqWAU8nGQEs72hQI4Ro3bIiwxH/i1BUCwfmwftnu7ENzPpfhC93gIXB2HD0UIvXv+vG67ZYX2o49OEg2yphvxyL+Rd7yfDv/upRFzIc80AtH2+0yUqEdy9NZOpANzt++A7l/16CcbuoGZRL3cZqrIwElg7uR63Pw8SqzQxYWUoQPwbItXaSaAJYRDiUGrBc1Jn5uH58BLUvrsbeXI57XA5pC36A638fwo/uA2PgV2fCrXOp/unr1N61gLDLoiBpCAVVPrx5fhKraig2Ln7w41PYlJxMeiCZc1dsIjUc4fJbhpMbCrD4mDcJlwXx5SRg7azCwjCUjWSwDosgJj0J663fwIGjYcl6OOo3UFIFM8bCe7eA37fbcyREe8QV2CilsoG7gdnRRW8C12qtS5RSeTgX+1nADpyLfQOt9T9blHUbcCNOQPAlMBIo1Fq/HN1khVLqA6C+a2Q4cAwwSmtdDpQrpf4I3ESTwAY4DXipSUblSuAaIA9YDvxMaz0/us4Cfg5cBSQBD0PjDf1a6wCwrMVpsHGCrvptno2WdXFr521PtXG+5wHHR7c7B7hLa/2b3ZSVCvwDmIMTHP66xfqLgZtaC8CUUgY4TGu9IDp/BPCO1tqjlLoeJ2tV3xaAdK11RCl1GvArnN/vduD2aHaovtzvAb8EcoCXcM7/bgO16HkpBAZrrbcppY4C3gUu1Vo/oJTyAMXAsVrrRUqpDdFje6y+3dH2/g7oh3NeL9VaVzYp/w6c8+7HCbB/rLXeEV1/NXBtdN8K4GGt9Y27a7Pouf612Kao1pleVACvrzcEI/DlDmeZwQLL8O5Gw8dbDUcMsbjvqwjbomN8l+40PLfc5tL93but5921ET7e6CSfS2rh75+G+M/hAcr/7dwRFYy4qNtY7dRZFiDbV8GWwVnsCGWQjdNAC6g1iSQSwODGxovbhDC4CPzjE2zjBA6RFTsJPv01/tuecYIagN89h/m/06i9awEAHtswrmobKxhGqKAOiwhvzhjFpuRkAMoTfCzLTuegghLefGIHR27fRrgs6LR1ZwAvFm6gDg9unOVWeQ385RV44lr45xtOUAPw6Up4azGccuAe/Y5Ex/T1Jw8/DmTidCeNx3ljf7TJughON9LhwMVtlHU0TvfM6uj8m8A2pdTpSimXUmq/aDn1gc4UoFxrvbZJGV8Cw5RSaU2WnY7TfYRSai5wG3AhTobnv8AbSqmh0W3Px7lAnYoT+BRF62xGKfWKUqoWWIeTefpPG8fW0mClVIFSarNS6qlokBaPVs+31npOdP3DWuuU3QU1UX8BRgMTgMk4x7z7d9o4aa3vaNGWlGhQcyxwP05gmQVcBPxDKXU4gFLqMOCfOF1qWcDbwNlx1FcMLMEJdMHpmlzTZP4gnABUt1KEGydomQKMAfYHro62ycL5+zHAfsBQnEDwiej6McAfgJO11qnARBr/RveayspKme6i6YEpzd/0063aXZZhwGUZ8pKd5Vm+5nccDUxtu64BqRZNry8D0yxqTABXmhOMuDDNygz5nM+jtqv5cjeRhmmLxl56KzWh2XaugWlE8tIb5u2cNEj2YWU13oodavIW4QJyKpv2qENyyPkMkpxukdBsMLLBamiv1bzlAzOdYx+Y2awsBmZ1+e+6p06L2NrM2CilBgLHAWO01qXRZdfhZFbygaNonk25BXirlbLG4IxV+b/6T8la62ql1IPAIzhdSW7gj1rr+jJSgfIWRZVFf6YBFUqpIcBwnE/Y4HQN/afJWJv7lVKXAecCv8cJeP6jtf4i2q7f41xkm9Fan6yUcgMH4GQ8ito6X018hDP+Zg2Qi3NRfFspNUVrXd3aTm2c7wFa6+2t7RujLBdOhuIkrXVBdNkNOEHg3vQT4K/1GTJgkVLqMZzz/lH057Na67ej6x9RSv0gzrLfwQlkHon+/BXw12hgcgzwfsuxVy38XGtdBVQppV4EVHT59OjrmGjGjmhGqig6ID6M88F4olJqo9a6DFgYZ5v3WGpqqkx30fRPlUVBtcXiQsO5410cM9rJWPz1KBdPrbCpqIOMBIsrproZl+1EJtccksSOugifbLE5dYyLE0a526xrWirce1oCD38ZYr/+Lm48wkeiNwHPy6dRfPOnpGT5SZ88kLJ3tuEbn0FJSjL51RGUK4A3HKKoKBGT6sef58NeFcRDiLJh6dhuNzm5uST/7mTqnl9BeP4GfCeNxXfqBJjwM7j2QQiEcP3xAvC4SXvlQqpveptv63zMTx/O+G0VjJ+dg7u4itNXV1CeXsqHRS4yC6qYVlbJwHHJnHPNMLxmKKHSINXflpE2LZO6zwoIri8lUuOl3AwiNQ/cR4yDW84hNdkPPz8DdlbA1xvhglmgRtH0kYFd/XvvSdMd1ZcHDw+O/lzfZFl99qT+PsKNTdY13a6BUmoCzifzP2mt72my/BLgZpyL0uc4n5SfVErdprX+Fc6n5vQWxWVEf9aHrqcBr2mtQ03a/HSLfdY2OZZBwIb6FVprWym1kRi01hFgYTTb8E8grsG/Wut1TWYLlFKX4wRoB+N0n7Rmd+d7ME63TrxygASaHCut/H462XDgyGhAVs8N1Ac6g9g1qxJvu94BHowO8B4DPIcT3EzB+Rt6cjf7RrTWO5vMV0PDe+pwnHO1QynVdJ86YIjW+pPoHWRXAvcppb7GueMqZhAver4Ej8Xfj941uXn1NBdXT4ud7Ha7LO44pv1DFy87wMtlB3ibLUuaNZik9wc3zA+K5mbHtNg31vN8P5o3D4A5c+YAkHzA0OYbjB4Ir/yy2SLvjCFkvHsphwCHxCizsd8/d5d14+6JtUcr/D745/fj317sNX05sNkc/TkMJ/sAzuBVcAbQghOMrG2yXTNKqWk4A35v01r/vcXq6cB7TbIr65VSjwOX4lywlgDpSqkRTYKF/YEN0SwROBmIf7Voc8t2jMAZnAzOwOSG9dFP+y3+83fhwenS2VMm+mrrL2l353vzLlvvXhEQjJbV6u+nDVVAcpP5gS3Wx8qObAQe0lrf2UqZzc5/k3at2WXLXc3H6V68CpgfHdz9Ds7fwEE4fzd7YiNOoJPVWsZHa/088LxSyoeT4XtJKZWtta6Jtb0QQoh9r83AJjpI8y3gLqXURTgX5ruA17XWm6MDfe+IZl4S2XVw6kzgFZxbvP/Lrj7G6UqYrrX+Qik1GGcMzBfR+tdHL1x3RAecZuPcGv6faPnZOF1Frzcp86FomS/jjMe5AJhKY7bl0Wh5LwDfAD/FGWtT3+aDcS7mn+AEBjNxulfubbKNJ3r+vICllPJH21sXXX8STlC2FWe8zO9xAo3ddl+0cb7bk60hOt7lCeAWpdRSoBanS6w9vgAuUkq9jxPUXNdifQFwsFLK1SQg+AvwkFJqIc45dON0y1laa41z/t+I3ir+IXAOTlDSZmCjta5VSn2C8zur/1t7N1pmgdZ6VTuPr6FonN/X35RSv9FaFyulcoCjtdZPKaXG4mR1PsI5j+U4geruur2EEKLb6uuDh8/H6fZZCazAGeNyYXTduTgp/M04n6YfabHv7ThdSXcrpaqavA4D0Fo/CdwJPKWUqsS5XXspzS+g50XbuhWnu+olGu++mgN8EB03QbTMJ4BbgMdw7pK5EjhRa13f3fQI8HecDM4OnNzqR03q80XLLwRKcYKov+F0mdW7CecCdy9ORqU2+qp3RPRYqnDusMrGuVunirbt7ny3109wunlW4ARx86DJKMO2/QgYhXN7/dM4QWNT9+EEgcVKqTKllDvaPXM5zu+1CKf77G6cZ++gtf4Q+HF03xKcu7za8xC9d3DGV9WP0fkA5+62d9pRRjPRoOxUnEDyi+jf4kKc3yM4fxO/jh5LGc6g4+/UB7JCCNHTGCv2q6ezTP0tfz1UdADoK1rr+7q6LULsBT37H1R0iXktxtiIXqtDYcj/nfFNzPeXu56f1KPDm97wyMdPiN7mLYQQQoj4mI7FRd1Wjw9sos9S6VGUUvfgdDfFMkFrvakdZd2I88DDWE5ocst1jxC986i15wX9oOlD/oQQQoiWenxXlBC9nPyDinaTrqg+o0Mpl2u+syzm+8tfnpvYo1M5PT5jI4QQQoj2663PsYn3righhBBCiG5PMjZCCCFEHyQZGyGEEEKIbk4yNkIIIUQfZPfOhI0ENkIIIURfJF1RQgghhBDdnGRshBBCiD7I7qVPHpaMjRBCCCF6DcnYCCGEEH1Qbx1jI4GNEEII0Qf11ruipCtKCCGEEL2GZGyEEKIbKH95HeGdtWScORp3mq/d+5d8U0rJkhL6H5Ib9z5vrAyxuczwnUkespKaf859f22Y1cWGSf0tvt4eYXq+GzVYLhm9iS1dUUIIIfaGgps/Y8ctiwAo/tc3jP7sLCxP/An1woU7ee+cDzBhgyfFg+9nbly5u9//7o8CXPdKHQB/nu/iy5+kkOh1LnQP6hDfey4AgIXBRGzcLnjrsmSOGiWXDdG9SVeUEEJ0sYqX1zdM1365k9DWqnbtv+297ZiwASBcFSayym5zn5e/DTVMryi0WV3UuM/Ly8MN0yZ6S3DEhtdWNO4jej5jWTFfPZ0ENqJXU0q9rpS6vqvbIcTuJB2S1zDtG56GJy+5Xfv3U9kN05bHwjW07bf2Q4Y1Zl4GpFoMy2zc55Ch7iZbmoapGUMlW9Ob2FbsV08nf6WiV9Nan9DVbRCiLfl/OZyEsZmEC2vJ/sF+uBLcbe/UxKDZ+Rz2wEyKvihm4FED+KxwYZv73DY7gSEZFpvLDJcc4CXN33hF++lhXvolWawpthmfa/Ftgc2Bg92ctp+33ccmxL4mgY0QQnQxy+Mi58dTOlTGoNn5DJqd78zMa3t7l8viBwcnxG6PZXGJkiCmtzO99MnDEtiIXSilNgD3AUcDBwDrgfOAicBtQA7wDHCF1jqslHoQOAbIADYDt2utn2hS3knAncAQ4ANgNbC/1vqI6HoDXAVcAowDlgEXa61XRNd7gOuBi4Hc6PqfaK11dP0x0fJHAkFgsdb6mOi6D4B3tNa3K6WGRY9lsNZ6S3T9xcBNWutRe3Lse3qORR/y2xfhdy/B4Cx48ToYl0/gow2UzX0OUx0k7e8nYvYfzIoz3ia4tZrkFEioqsJ/ySTe2+DCs6GImWuX4w+HWDN0IDM2adyhWiwMKzOGsSB7Gv0Py2NQIEzhy5vJsEuYbRbiNmHIfgmevBYOn0hRaYSb7ipkS0GEsMeiKM1HWa3TRAPUeS3+dH4qc6cl8PSSIFc8X4fPA+OHJ7KwAA4eaPHiGW7S/c0vhm+ut7nodZuQDffOdvGdMY1dWg8vtbn6PZtAxKnjuGEWz8xxkRAMYp9xB6H3l/LWmCn898breOq7CSR5e+eFVuxbMsZGtOYi4IdAJrAEeAE4EpgCTAJOAc6ObrsAmIoT2NwKPKSUmgCglBoJPI8TFGQAdwOXxqjvYuA7QD+c4OjvTdbdApwKHA9kAw8AbyilMqPrHwH+BqQD+cDte3zUjvYcuxCtW1MANz0NNQFYuR1ueAqAiitewd5WiSkPUH7Zy2y49lPqVldg10SoLIwQrolQ+8+vMOvLmbZpLWl1tfjCYSavXYUrFMSFjYVhXNl6hlRvZ+dHBax/dSt20FAaSsdXF8ATCMO2ErjyXgCeeqWCLQURALa7PA1BDYAFJIYMVzxeRShsc8nTtZTWGnYEXHywBerC8MEmw9++2HVQ8uVv2eyogZI6uOQNG2OcMTl1YcPlb9lUBCEQgWAE5q01PLTMwL1v4XrzKxKCIeYs1fR//gPu/drsUrbYu2zLivnq6SSwEa25V2u9XGsdAp4ARgC/1FpXa6034WReFIDW+n6tdbHWOqK1fgr4GjgiWs5c4DOt9ZNa67DW+l3gpRj13am13qS1DgAP1ZetlLKAq4Gfaa3XReu4H9gOnBTdN4iTremvtQ5orT/YV8e+t1VWVsp0L5huqrXljfbOBT4UjP+Opt1d2zrnGNtfvkzvOt1REtiIvmZ7k+kaIKK13tliWapSyqWUulUptVIpVa6UKsPJbOREt8sHNrYou+V8y/qqgdTodD8gBZinlCqrf+EEG4Oi25wKjAa+UUp9q5S6ph3HGUtcx97BOuKSmpoq0z15elQe3H4WJCfAuIHwx3NITU0l7Z6TceWnYmX4Sb/vFIbdPQP/6DRcSW5Scz14kj0kXrU/1vB0vhwykgp/IkGPh69HjsH2+rBxYSyLFRnD2ZQ8gJzD8xh+Uj4un0WWt5ygP4Gw3wP5WfDv7wNwwRnZDB7gDEoeYIfJSGxoqtMV5bO457wUvB4XD5yZSGaiRf8EmyMGgd8DRwyxuHq6a5djvO84F/2TIMsPDx7vwrIsUlNT8Xss/jvbRZoP/G7wueGUkRYXT7Tg+7OJHL8/AZ+XefspdpxxBN+fbHX976uHTYvYZIyN6Ki5wGXAbOBbrbWtlNLQMCpta3RdU0PaUX4RTqBzjNb681gbaK2XAGdHszuHAm8ppb7WWr/XYtP6jzpN76Ud2I62CNF+vzzNeTWRcPgw+m/5v2bLpq/atXfzkhbzzh/rDxrmx0VfLc2bNxyAOXPmNCzrl+nm37cNiKvJZ03xcdaU+J5+/P/t3Xl4VNX5wPHvmewhCYGEsK8CKqIiHlesW+uKaFu1VmsBd221ta3a1rrbn9atrdVatS6416q4oCLu+/pKBRXZd8IWSAJkz8z5/XFuYAiTMAkhy+T9PM883PXc996ZcN97zrn3Hj0oxKpfxL5GnjAyxISRMeYlp5E09WqSgHHBR7W+RLi1OxZNbNSOygFqgbVAKOiMuzfwcjD/P8DV1tqfAM8BhwI/BKbHU7iIOGvtncDt1tpzRWSetTYLGAN8jU98TgdeEZEia20xEAHCMcpaZ61dApxtrb0SGAGcF2tZpVpT7fQVVD37LUkje5J+xo7dHRX3NsOOF94vp2RjhHGHZNKze9NuMVeqvdKmKLWjHgE+A+bja2dGAB/UzRSR+cCp+A7ApcDvgMeAqiZs41p8v5wXrbUb8HdVXciW3+9pwGxr7SbgJeBaEXmvgbImACcEsfwVeLAJcSjV4sKLiyk59AEqbn6PTT/7L5X/jlkx2eLufnYDdz69gUde3cTFt6+jqkY773Y2EUzMT0dn6nqwK9VarLVPARtF5Py2jqUD0D/QBFf14iw2/vCJzeNpZ40m+6GTd6jMKVP8g2yim6LqO/emIuYs3dKh+Ok/96BPvlbidzA7lIWcNnFJzP9fnp40sENnN/orVjudtfZE/C3hG/B3Mp0MHNOmQSnVTqQcOABT0AW3pgyMIfWEWL1mWt6YvdM2JzbD+idT0E2bolRi0MRGtYZD8c+eSQeW4h9u907bhqRU+xDqmUWu/IKaqXNJ2qMnKWMGtsp2zxqbzfD+KRRvjHDE6HSSkzr0RbpqBu08rFQzichlwGVtHYdS7VVS/1ySzt+/1bc7Zq/0Vt+maj8S4Zk1sWjnYaWUUkolDK2xUUoppTqhRLgDKhatsVFKKaVUwtAaG6WUUqoTCidmhY0mNkoppVRnpJ2HlVJKKaXaOa2xUUoppTqhRH2OjdbYKKWUUiphaI2NUkop1Qkl6u3emtgopZRSnVBYOw8rpZRSSrVvWmOjlFJKdULaeVgppZRSqp3TxEYppdqBcEklZe8uo/TDldQWV1L14iyqP1zcyAoR+N9iKCzeZpaLQFlRChvW1zS4+oYqx5crI2yqdjsUd1m148tVjpLKHStHtb4wJuano9OmKKWUamPV84tZdtCTRIoqqCWESYqQFy4BIO1ne5Hz+E+2XiEcgRNug9dmQloKTL4Ujh/lZ4Ud303Np3RFOrNe/paf/2kIw0bnbLX64hLHmEdqKNwEg7rCxxNS6J3d9BNaUbnj4KfCzCuGnpnw4elJDO3W8U+MnUWivlJBa2yUUqqNbZj0LZGiCgCSiZAb3rB5XtWTM3G14a1XmLnUJzUAVTXwj2mbZxUuKKd0RToAtdWOT15eu832Hv8mQuEmP7y4FJ6ZHWlW3JPnOeYFFUary+GRb5tXjlItSRMb1WKstROttfNbqCxnrT2kJcpq5vYPt9bWttX2VeeSMnDrGpWaqMp0k52GSU7aeoWeXX1NTZ2B+ZsHs7ulYEJbmoVyC1K32d7ArvXGc5p36V4v7GaXo9pGxJiYn45Om6I6CWvtu8CbIvLnOJe/DjhERH6wM+NSSkHOuXtSs3wjJY/Ppio5nbQ9swl98A0mK43s//502xX6dPPNT/+YBgPy4LbTN8/K7ZHK8KPWserbLHYd2ZdjJvTZZvUzR4ZYWgrvLY1wzJAQJ+3avGvcYwaH+MeRMGWB4+A+cM6eHf+kqDo+TWyUUqqNGWPIv34M+dePiZp6YuMrHT9qc7+a+roPrKT7wErGjTuowe396ZAk/kRSzPlNccnoEJeM3uFiVBtI1Af0aWLTCVhr7wa+Bxxkrf0DsALYA7gSmAh0A6YDvxaRb6y1pwXzQtbaoCWevYBq4AFgXyAVmAlcKiJfNjGew4E3gXOAG4Bs4CXgYhHZFLXoXtbavwG7Ad8CE0VkdlBGMnBFEH9BMP/XIiLB/ElAElAJnAqUATeIyH1RcZwMXAMMAhYD14nI8w3E/APgNmCX4Dh8pbVZqjlcbYSiP7xP5acryfrRULr9bj9WvLaCWX/7loo5pWSX1TCk+1r6rJoF/fJIfv+PmLxs1i/cxEd3fEf2wrUMKVpLeqSK3OR1JB88GHPrKayftpzS86ewdn0KmaRSkt+Fe29/m7LeXdmYl0O3nBCHnZjPtBXw5TuroKyMNX17cMD+3TlvTDqnPFnF4lI4YnCIJ996iIzJH0Hf7vD+//F5ZRZXvlVDZgr8/dgUhnTfUsNz86cRXlkY4ZC+hpsODRFqwsnyk0LHVR9GyEqBO48MMahrYp5o26tEbWvXxKYTEJGLrbUjiWqKstb+ERgPHA8sAv4IvGGt3VVEnrbW7k69pihr7QDgHnxS4oC/AJOttUNFpOH7SmNLAsbhE6Z04Hngr8D5UctMBE4GVgOPA3cBRwXzrgd+ABwLLAmWfc1aO0xE6u5/PQU4DbgA+CHwtLX2NRFZYq09GHgC+BHwBnAM8Jy19jAR+SxGvI8CfwIm4ZO62JfCSm1H6T1fUXKHAFD50Qoi/bryyRUziFT7jrdJNTX0n/sJIRzMKqF23N9J/vhqXrvsf5TPKWbEd98RcY5yADaS9/kbhLtnsfqGuVSGkygxmQB0WVnF0n6GmtUVlJUnsT41hffvXceMnGwqQ91Zm5sPZfDJO5X85+swSyuSwBhemB3hz4u78X8bKmDDCiLjbmLsj66lyG+QteXVfHKu75z80vwIV37g4/5ohWNIruP8veNLTmojjhMmh1lf6cfXV4b54HQ9Jakdp52HO6+zgFtEZLaIVOFrTsLA2IZWEJGlIvKSiJSLSAVwFTAAGNbMGH4vIqUishpfczLeWhv9m7wt2GYVPqGwANZaA/wKuFxEFopIWEQeBFbWi//tIN6IiEwGSoBRwbyJwHMiMlVEakXkFXxydXYDsVbja2t6ikiViLzbzH1uko0bN+pwgg2XL15PtMpFGzYnNQCRkPFJTd34Kp+nl6+tJKW2liS3ZV44uDatnreGpNqarTsdA8nV/m6qpEgEh6EieEZJ/dt8iysdRNW0FGZ127L9tRtYV75lmytKt8S6sKhyq3IKN7m4j0NRyabNSY1ft/HldXjb4R0VNibmp6PTxKbz6o+vqQFARCL45pj+Da1grc231j5qrV1qrd0ALAtm9WhmDEuihhcDaUB+1LSVUcNl+CYrgmWygCnW2pK6DzAE6NfA+vXL2Gr/AwtoeP9PwidwX1trZ1lrL21guRaVnZ2twwk2nP9LS1LvLgCk7plPzwtH0m9s8LN1jrSyCEXG/0k5EyLpxlMBsOcPZVNGOiu7+luajHHkUAy9upJ51fGExu1Bvisl5HwyU56dSllOGjhHeXoqSS7CUbsm0zMzQoaDLrW+krV3tuEPR/jlANKTHL+a9Z6Px0DydadxxRh/B1bIwNWHb7nLavzeGeye54f7Z8PZe4biPg69umfzO+tPokkGrjow/nV1WDVG6/06j/oPmFiG71sCQFBTMogtyUqsB1LcDPQGDhCRldbabGADNPtRlQPxyQTBtquAojjWK8InKT8QkS+aue2t9j8whC37vxURmQGcFtQWHQK8bq2dKSJvN3P7qpNK3SWXQXPPoWbpBlJ2ySWUlsxB/z6YjQs2UlNSjSutJnfUT6j9dAGhkb1JCm7lHvXzwexyVC8iYUfmpgpMdgpJpZugXzdMdgbDXjqBsun702PNJt6T/5HiHBOO251IfheSU0MYBz36pHJBNaxYUkZBxUZW5GUzoEcKWWmGM/cN890ax4EDkuh65fXw5kwYOQAGFvAX4Px9k0hLNvSNuqW7e4Zh+vgkFpb4W7+7pDbtv4LbD0/iolGOjGTok9Xxawo6mtoEPeSa2HQeq4ChUeOTgCuste/ja0t+j/89vBK1/ABrbaqIVAfTcoByoNhamwXcsoMx3WytPRffx+Y64LGg5qhRIuKstXcCt1trzxWReUE8Y4CvRaQwjm0/ArxprX0M32foaODHwOH1F7TWpgKnA6+ISJG1thif+IXrL6tUPEJZqaSN2FI5aYwhe2AXKmatJtQ1jeS8TBi75zbrZffKCIZ8Pxr6bP0gmS6jC4ACqPkWB/S2+dSXmQbDhmcBWUQ/zmZAbhIDcuvGUmGs3Wq96A7D0dKTDSO23UzcdslN0LNrB1CbAK9PiEUTm87jb8DDQZPNCnxfkzTgdaAr8BVwtIjUPfL0GXzH21VBbc4++H4wk4B1+A6917B1Z9+mCOOTqK/xCdMU4LdNWP9afD+bF621/fA1OJ8Cl8Szsoh8ZK2dANyOrzlaApwpIp82sMppwB3W2nRgDXCtiLzXhHiVatSak56jYupCAHKvP4Tca9rs+ZRKdWjGRXVEU6o11N3uLSKaWG+f/oF2AuF1FSzLv3PzePKwbvSbe0Gzy5syZQoA48aN2+HYVLu2Q1UuQy9ZG/P/l/l39ejQVTnaeVgppdpYKDeN5EFbGoZS9y5ow2iU6tj0ilntFFEP9qvvA3a8b45SCcUkhej59ulsuONzQl3T6PqHA9s6JNUJ1CTArd2xaGKjdgoRydrOIvrbUypKyuBc8u4+uq3DUJ1IU5+q2lFoU5RSSimlEoZeNSullFKdUHmCNkVpjY1SSimlEobW2CillFKdUEViVthoYqOUUkp1RtUJ+uRhbYpSSimlVMLQGhullFKqM0rMChtNbJRSSqlOSe+KUkoppZRq3zSxUUoppVTC0MRGKaWUUglD+9gopZRSnZH2sVFKKdXSnp4dYd9HazlxcphVm1yLlPn48l349TcHcPHL1dSGW6ZMlYBMA58OThMbpZRqIys3Oc58JcL01TBlgePSdyI7XObzs8L8d+VgFlVk88/Pw9wn4RaIVKmOQ5uilFKqjZRWQW1ULlNUvuNlFpW7RseV2iIBqmdi0BobpZRqIwWRWs7pUUGvjeV0S4pw1UFNO9E451g/bwNlayo3TzttZBK7pJWSW13DsKwI5+275fq1urCM8q/X4ZwmOypxaY2NUkq1gU/fKua/96ygX1k5FzlHt77p7H/uCOK93nTO8ebl01nwWiGhZMMPbhvNkKP6EKqq5YhvitmQVkXqslqKPs2mz9EFFL+wiAWnvYGrjtDt5CHs8sxRmATtPKrilKBfv9bYtDJr7QRr7XJr7SZr7cnW2knW2gfaOq54WGsHBHH3aWSZN62117XAtq6z1r65o+XsYAzvWmuvassYVOJ668V1pFbXEApqT4pXVDL7w+K419+wrJwFrxUCEKl1zJi0EID3Jq9hQ1omANUpybzyTBEAq+6Ygav27V7Fzy2kal5pi+2L6qAStPOw1ti0ImttMnAPcKqIvBpMG9e2UcVPRJYCWW0dh1KJIDcvhdLFW59Fcnqkxr1+etcUkjOSqK3wnYOzemUA0HtAOrBp83Ldc/31a2r/LX+6ocxkkvPSmxu6Uu2aJjatqxeQCcxs60CUUm1jfXEtr76+kdSBXViy1lCwagNdwzVk9MlgzhclpGYkMWBkNk99G+bTQsfx/R19X51LbVktg88ezr3L01i1IcJxXy1kYH4Kq1ZHSMlOZuSP+lFYGuE/FZks6raBDZXpDKmuImvlOj7Y/VGGVBWROiyTpdm96f+H0Q0mNpFJHxJ5ZjrhjEySzjqI5LF74Cqq4a9ToaQcLjkKMyC/4R2MROBf02BeIUw4AvYZsmXeWzPhpS/ggGFwxqEtfGRV0yVA9UwMmtjUY61dDNwPfB84AFgMnC8iHwc1LlcCE4FuwHTg1yLyTbDuJCAJqAROBcqAG0TkPmvtQUBd08oca60D8mJs/ybgp0ABsBq4S0T+Hsx7BlghIpdGLT8RuBoYCvQFHgD2BVLxCdSlIvJlsOx1wPeAz4BzgyL+JSLXRpV3GHArsBuwEvibiNwXzBsELAL6i8hya60B/gD8Ep+wPUKcfynW2neBr4K4DweWAJeJyNSoxUxwPBqKdSRwBzAaqACeAK4RkZqoWMcDfwT6A58AE0RkZbB+HvA34OigyGnAb0RkfYx404C7gB8C6fjv5koReSae/VUKfL+YG29ZTeGqWpanpZJTA9mpaVSQRqQY5r9XyVevfkfB7/fi/A99Tcs/v3Dc+PQyBq8t5cpVOUwd3B+ARzf14R9fzSDFAKvgwwkfcPW5RzFnPZDcHbJgrssgp7Caq9a8T+761eQCLx3eiwmrezO33JGfufWfa+SB94mc9wjg+ylUPzcT8/6vCd33OjzxsV9osuDm3IpJToq9kzc9B1c/5Ycffgfm3AW9usH/FsKxN0JtcPt5KAQ/PaTFjq1SdbSPTWxnA78CugJv4E/YAJfjT5TH42tfPgDesNbmRK17CjAF6A5cAtxtrR0oIp8AewTL7CoiWSJSFWPbs4BDgGzgPOBma+0xwbyHgTOstSlRy58FTBIRh/8+7wEGBvFNBybXW/5QYCnQBzgRuNJaOwbAWjsYeA34Fz7pmhhs/9QGjtOZwG+Ak4LtFQXlx+sc4E4gF7gJeD5ISOKJtQB4D5iMT+gOAo7CJzHRTgvK6Qt0AW6ImvcEPkHdPfjkA481EOsEYD9gdxHJAY4Evm3CvjbLxo0bdTiBhisqHYWragGoTArRJbzlGTMhoDIlhXCN44N5W+5yChvD4vxcAOZkdNk8fX12JuuzMzePl9Xgk5oozhjWpqWyPrvr5mmjly+nuBIWlG4bZ/Un8zYP++4WYSLTlxP5fMGWQheugeKyhvdXopbdUE75jGD8q0Vbkhqg+uNZ266rw00a3mEJ2sdGE5vY7hORb0UkjK8BGWqt7YpPIm4RkdlBUnIDEAbGRq37toi8JCIREZkMlACj4t2wiDwuIoUi4kTkbeAVfO0R+BqFWuAEAGvtLsAYYFKw7tJg2+UiUgFcBQwAhkVtYq6I3CsitSLyKb7WxAbzTgemi8ikqPn3saXGpL7xwbH6UkSqgZuBVfHuK/CCiLwRbOsJQIAz4ox1PDBDRO4TkWoRWRFsf3y9bVwvIkUisgF4sm79oAP0McBvRaRYRIqB3wLHW2t7x4i1Gt+/aIS1NllElonIrBjLtajs7GwdTqDhzIwQe4/0TUDZtWGKU1Kou/G6xhi6VlSQ1T2FMw/MJDWoEOnqahm5fC0APwhv2FzmsBXr6FlcBkHn4+4FqRy9S/BfejAtLRxh0MZy8ktKNq/3wp57sVt3GJm3bZypPzkAkvyZzWGIdMkk6ZjdCJ124OZlOHx3yM9ueH9POWjLo/qH9ibz4OB67sg9IS9YJiWZ1FMP2XZdHW7S8I5LzMxGm6JiWxk1XBb8m41vzlhUN0NEIkHTVf8G1q1bP+5forX2V/iamn74X1gG/oSMiISttY/iE6zn8TUqb4nIsmDdfOCv+KadXKDu0V894oxvq/0LLMDXyMTSD99URxBfxFq7ZLs7ucXiGOP9osYbi3UwMMZaWxI13+CbAqPV/y6j9xW23t8FUfPqb/txoCe+6WqYtfYt4AoRmY9STXDFpQV8Mb2ctDTDF0vC/OeTVFZvitCjbxp37Btm19E5ZOelMr1HhOmrHIcNSCH5QEvtphpOPbYvp60yrC6DA5aEcYP3orq4mqTeGQw8eRDHdk3lv19U8ex/lrOmKp09uxsuPAKqk/ehPL2KZceN4PA9h3LrEEOX1G1PYKFjRmLkGiJvzIL0DDLG7kFoSD7ceArukOG+j81Joxu/TfzMw2BoL1iwCo4dDdm+UzMDC2D67fD+LBg1CEYO3DkHWHV6mtg0zTJgUN2ItTYUjC9ricKDZpZb8DU0nwWJzLNsnUJPAmYGtQrjgd9HzbsZ6A0cICIrrbXZwAbiT8GX4ZvZog2h4f1bwdbHw+CbweI1KMb4q3GuuwR4U0TGbnfJ2Or2aRBQl5wMqTdvMxGpxX83t1hrc4G7gYdoWtObUiQnGw7a3zcpfV5exbu1EUiH79bB510ysXn+zqg9eoTYo+6S5MgtlYhHDggGdu8Dx2775IXI4nLSSlPoT5iSQkj62WD2+uUIwF/hjN5OfGbUAJJGDdh2+jF7xb+TB+7qP/UN6OETH9U+dPzKmZg0sWmaScAV1tr38bULv8cfw1daqPwcfNPWWsBZa8cCxwGbO6iKyGxrrQAP4msfnq+3fjlQbK3Nwp+Im+Ip4Gpr7Xh8LdFo4ALgogaWfwy41Vr7PPA1cBm+r028fmit/T7wLvATfDPRz+Nc91Hgd9bas4NYq/FJynAReW17K4tIobX2deAOa+0E/J/4HcDUus7F0ay1RwKl+A7ZFfjaH30Jj9oh4Ujj480RiTQ+rlSi0z42TXMb/uT/Ov6umCOBo4P+Gy1hGv6E/Tm+I+4pbJ241HkYn/A8Wa8D8jX4u6nW4U/AH9OEk6+ILMLX2FwclPEYcLWI/LeBVR7F3yk0BX88CoD3490ePjn7LT5huAY4OYghnlhXAUfg71JaDBTjj9WQhtfaxpnARmAOMBvfH6p+H506PfHHoxjfTDUQOL8J21JqG2ftl8rRw5NJCsFRw5I5Z//4n2PTkJPG5tAttwJjHIcd0mVznx6ltpGYXWww+s4Q1RaC273fFJE/t3Us7Zz+gXYCzrkWfb3BlClTcA5OPLHDPP9TNc8O/WjMHzfF/P/F3ZzVodMbrbFRSqk2tjPe2aSvgVKdlfaxUTuFtfZK/MMMYzmuNWNRSikVQ4Imv5rYqJ1CRG7CP3SvIYe3UihKKaU6EU1slFJKqc4oQdsrtY+NUkoppRKGJjZKKaWUShjaFKWUUkp1RonZEqU1NkoppZRKHFpjo5RSSnVKiVllo4mNUkop1RklZl6jTVFKKaWUShxaY6OUUkp1Rlpjo5RSSinVvmmNjVJKKdUpJWaVjSY2SinVQX3zciErZpYy+KA8hh9R0NbhqI4mMfMaTWyUUqojmvv2Gl6/aTYA376ykp/8czT9RuW2bVBKtQPax0YppTqgtQs2bRlxUBQ9rlQnpomNUkq1c865baYNO6wHKRlJ4BwZuSkMPjgvrjKcczHLU52QaeDTwWlTlFJKtVOusoaNP36Cmtfnk3zYIHJeGo/pkgpA115p9O3uWLdyI31H9iQ7Py1mGWUry3nr7I/g60JsyTKSq2qojiTD4HwKppxK6u75rblLSu10WmOjmsVaO8ha66y1/do6luaw1k611l7R1nEo1ZiqR6ZTM3UuhCPUvr2Qyvs/3zzv2ycWUfR1CS4Cyz9YzYKpK2KWMfOfsymeXcrua1eQXFYFtRFSItXULiim5Mr3WmtXVHtkTOxPB6c1Nmq7rLWHA2+KSML8XkTkuLaOQantSgo1OG5CW5+A6o/Xn+5itTEkdfyTmFL1JcyJqrOy1qaISE1bx6GUaprPCyPMXQ/HDjGEK8J8PbeaoQNS6Fu+kapPCynul8e6/gPJH7sn6W/PpmLUAGaM2J3h136MK4uQvEs2A7uUUViVSe4+PSjvkkHFtHnUvDSb7K6lFPXKYcqNs2FtDf1dJfO69mHPykKSyiupiCSR2iedbrccAUDhJsdbSxx79jCMKtiS7ETWbqJ22lxCw/NJ3n8AnxQ6li4vY+ys6WQNyYND92irw6dUg4x2Imt/rLWLgYeAo4FRwGzgIhH5wlo7CUgBaoATgadF5CJr7UXApUAv4DvgchH5IChvb+AfwB5AEvApcLGILAjmTwqmVwKnAmXADSJyn7W2D7AASA+mA/wSeA9YBIwH/gj0Bz4BJojIyqDcPOBvwX4ATAN+IyLrg/lZwHXAj4EewDLgAiALeBToKyLVwbLZwErgOBH5wFp7E/BToABYDdwlIn8Plh0UR2zv4muh/hzn8r8CfgPkAxuAR0Tkyka+xpaif6AJ6OnvIpz+YhgHDE8PM3LResrKHckh+O1rb5BWXs0b+47CGUNybZihs5YRCkfoWVlMbWk6vVjLAFYBsC4ti2mDR9MtVMlxs3xTVWUohUkHHcO6vO4ADFi9hu9/NoOlSd3pEq6imnTAMfKcXGr//iP2eTTM6nJfgfPqySGOHhQiUlLBptF3Elm0Hozh+Qcv4qLVPfn0H1cyauUSvyN3nQsXH98GR1AFdqjKzdxQGfP/F3dNeoeuytM+Nu3XhcCvge7As8Cr1tqcYN6pwFR8MvA7a+3pwI34E3Me8G/gNWvtwGB5h08g+gKDgE3A4/W2dwowJdjeJcDd1tqBIlIIHAeERSQr+DwStd5pwKFB2V2AG6LmPQF0A3YPPvnAY1HzHwQOAL4P5OATtZX4BKgMOClq2dOBZXXJGjALOATIBs4DbrbWHlNvnxqLLZaYy1trhwN/AU4QkWx8gvjSdspSqkHPzo5szljL11RTVu7HaiMwo6A3i3sW4IK+DrXJSZRnpZMarqVLaS1g6E7p5rLyqjaRU1nOiMLFm6elR2roEq7aPL60ZwFJSWFywmVBUgNgWPviMj5Y7lhd7qeEHTw/z8cSluU+qQFwjufmw7C1K7ckNQDPftJSh0SpFqOJTfv1oIh8GdRY3AJUACcE8z4UkadFJCwi5cBZwH0i8pmI1IrIg8BM4AwAEZkpIu+ISJWIlALXAwdaazOjtve2iLwkIhERmQyU4GuLtud6ESkSkQ3Ak4AFCGp6jgF+KyLFIlIM/BY43lrb21pbAPwEuFBEFomIE5H5wScCPACcE7Wdc4JpBPv0uIgUBuu9DbyCT5C2G1tT9wXwZxPYw1qbJSIlIvJpHMdmh23cuFGHE3DY9t5yQVzbJZlQ1P/EA4vWkV+6YfO4iURIr6gmHErCpfiko4yMzfPLk1MpT06lsNuWu5tqTYjy5C13SXXbuBEThnKTTojazdOz98hhl8xyUpO2bN/28rFV9MuErNTN0/fNDbOkWz5ruuRsnla9V/+dcnx0OL7hHZeY93trH5v2a3HdgIg4a+1SoF/9eYH+wH/rTVsQTMdauwtwG752JJstzRs9gLrLr5X11i8Llt2e6PWi16n7H29RvZii5wHMbaDcB4GrrbUDgK74JGts3cygaeg8/DExQAY+GYkntobEXF5EFlprfwZcBDxgrZ2Jb6p7fTvl7bDs7GwdTsDhKw5wdE2DOevgzJEZmPUhPptZyYhdUtlv3H5UvLWY3L4ZrM/JoXdXh/smmcXpWXybl4l96jOqq4cza1AKlbPWsaRrb9yuvQnvn0d4fh/c2wtY2CeJ3sM2kbkyi/T1mxi6bAVzDhtF7+ISUhesJClUTdcxvej90hn0T0nmzVMdz82NMKrAMHGkz7JydutL7TsXUPPE/wjt2oNrzh9MwQx4sP8NnPPJmxQMzyf1V2ObvO863HLDKjZNbNqvQXUD1loDDACWAyOASL1ll0UvHxiCb1oCuBcoBPYSkXXW2pHA18SfmtffXjyWBf8OAuZHxVQ3LxwMD8M3K21FRFZaa1/B10Z1A14QkSIAa+0YfC3W94HPRCRsrX2WnXipEdRiTbbWpuKbCV+01uYFNWZKNYkxhgv3iaom6ZXO6BF1TUTDyDxxGFs/bm8g+9QN/sJfFyybXcb9v/WvVGBlhF27pdLtNn+z30dTptAVOHPcfptLWHn5hxTdPgdHiNqkDPL+Pg6T4k8B3+tn+F6/qHgCybY/yXbLdcgv9wH2GQATzm7urqv2pONXzsSkiU37dba19nl8AvIbIBPf3HJ0jGUnAXdaa18CpgM/x9dwnB7MzwHmASXW2ny239ekvlVAkrV2sIgs2u7SgIgUWmtfB+6w1k7A/wndAUyN6pD7LHCPtXYivuZol2DdukTofuA+fH+Xn0YVn4NPjNYCzlo7Ft8P6Jkm7ldcrLW7AoOB9/FNgqX4Wq/mJHxKtYjSouqtx9dWN7CkV7Msqgkj7Kgp3ETart12RmhKtSntY9N+3Y+/k6kY36l1bNA/Zhsi8iS+38zjwDp8k8nxIlLXzPQb4Hv4u3k+AF5uSiAiMhf4F/C5tbbEWvvzOFc9E9gIzMHf2VWC7+Bc52zgK/wdVhuBF/F3ddV5HZ88lAJvRU2fhr9r6nOgCN/x+fmm7FMTpQLX4JuqSoBfASeLSOVO3KZSjRq2bw69d/F9bTKyk9jv+MafIJx3yd6EslIA6HJoHzLH9NnpMSrVFvR273YouN37KhGpf+dSpxPclv26iNzU1rG0Ef0DVQ2qrYmwdlkluT1SycjeUgE/ZYpvhR43btzWyxdVUFNYRvqI7phkva5NADt2u/efq2Lf7n1VWodupNKmKNVuWWsPBfbD396ulKonOSVE7yGZ21+wbvn8DJLzM7a/oFIdmCY2ql2y1n4BDAUuEZG1bR2PUkqpjkETm3ZIRAa1dQxtTUT22/5SSimlmi0BXngZizayKqWUUiphaI2NUkop1RklZoWN1tgopZRSKnFoYqOUUkqphKFNUUoppVRnpE1RSimllFLtm9bYKKWUUp1SYlbZaI2NUkoppRKG1tgopZRSnVFiVthojY1SSimlEocmNkoppZRKGNoUpZRSSnVG2hSllFJKKdW+aWKjlFJKqYShTVFKKaVUZ6RNUUoppZRS7ZsmNkoppZRKGJrYKKWUUp2RMbE/2yxmFhtjRrZBhM2iiY1SSimlEoYmNkoppVRnZBr4xLOqMeONMV8bY2YaY543xhQE0z8xxuwXDN9jjPk2GE42xhQZY7rshD3ZiiY2SimllIpb0Cz1F+Bo59xewDfAXcHst4DvB8OHABXGmN7AfsB3zrmynR2f3u6tVDtmjJkG5LdUecnJyfm1tbVFLVXezqAxtgyNsWW08xhfc84d29yV3WXJzb3h+wjgVefcymD8PmBGMPwW8CdjzBPAOuA9fKIzGHi7ubE2hSY2SrVjO/KfVizWWhER25JltjSNsWVojC2jI8TYznwMjAbG4pOc94Cz8YnNNa0RgDZFKaWUUqop3gGON8b0CsbPA94AcM5VAdOBPwBvAp8CY4C9guGdTmtslFJKKbU9bxpjaqPG/wi8YYxxwELggqh5b+H71HzhnAsbY+YDi5xz1a0RqCY2SnUu97d1AHHQGFuGxtgyOkKMO5VzblADsx5pYPmbgZujxo/fCWE1yDjnWnN7SimllFI7jfaxUUoppVTC0KYopRKUtTYTeBjYF6gFLhORl2MsNwp4CH+hkwJ8BFwiIlXtLM6T8HdVpOEfI/aQiNzRzmLsCzyOvytk3s6+m8ZaOxzfHJCHv7V2vIjMq7dMEvAP4FjAAX8RkQd2ZlzNiPFo4CZgT+AuEbmsteJrQoxXAz8FwkANcKWITGvNOFV8tMZGqcR1GbBBRIYC44AHrLVZMZabAxwoIqPwJ5Y8tu4IuLPFG+cqYJyIjAQOBi6y1n6vncW4CZ98ndFKcd0L/FNEhgP/xD9PpL6fAUOBYcBBwHXW2kGtFB/EF+NC4FzgtlaMK1o8MX4O7Ccie+FvX37aWpvRijGqOGlio1TiOo3gP+jg6lOA4+ovJCIVIlJ3t0IKkAFEWitI4o/zMxEpDIZLge+Age0sxlIR+QDY6U9XtdYW4GuGngomPQWMttb2qLfoacC/RSQiImuBF4BTd3Z8TYlRROaLyFf42rBW1YQYp4lIeTA6E19rmNdqgaq4aWKjVOIaACyJGl8K9I+1oLW2j7X2K6AI2Ejr3gkSd5x1rLW7AQfSSk8ypRkxtoL+wAoRCQME/xaybVxtGXu8Mbal5sQ4HlggIstbIT7VRNrHRqkOylo7HX/SiqVnU8oKakJGWWu74PuI/Bj4z45F6LVknEF5vYEXgV/U1eDsqJaOUSUua+1hwI3AUW0di4pNExulOigRGd3YfGvtUnxTzdpg0gD8E0MbK7PMWvs0vl9GiyQ2LRln0GzwJnCriDzTEvG1dIytaBnQ11qbJCLhoJNwn2B6tLrYvwjG69fgtIcY21LcMVprD8In/ieJyJxWjlPFSZuilEpczxB0ArbWDsM/CfS1+gtZa4dYa9OC4VTgJODrdhhnHv6x7XeLyIOtGB/EGWNrEpE1wFfA6cGk04H/Bf1ooj0DnGetDQX9Rn4IPNvOYmwz8cZord0PeBo4RUSmt2qQqkn0AX1KJaigWWkSsA/+FtUrROTFYN4NQKGI3GutPRP4Pb7DcBL+pXWXiUhFO4vzNuBi/F1cde4UkYfbUYxJ+NqQNKArsAZ4QESu20lx7Ya/TbkbUIy/TXmOtfZV4BoRkSCmu4Gjg9VuEZFW60MVZ4yH4GsIc/CdckuBc1rrduo4Y/wCGASsiFr15yLSmhcBKg6a2CillFIqYWhTlFJKKaUShiY2SimllEoYmtgopZRSKmFoYqOUUkqphKGJjVJKKaUShiY2Sql2yxgzyBjjjDH9dvJ2LjTGPBY1PtUYc8XO3KaKzRgz3xgzMc5lW+X30RqMMWnBvu/W1rF0dJrYKJUAjDFDjDHPGGNWGWM2GWOWGWOeN8akBvMnGmPmx1ivoek/C04Y18aY964xpirYTqkx5n/GmJN3zp7tfMaYLsANwHV105xzxznnbm2zoLYj+G4Oaes4OoOdcayNMYcbY7Z64adzrgr/dvO2esN5wtDERqnE8CqwEtgVyAYOAqbhH3bWHBcA64FzjDFJMebf6JzLwr/d+CngaWPM8GZuq62dCXztnFvQ1oGoTu8p4EhjzNC2DqQj08RGqQ7OGJOHT2judc6VOm+5c+7e4CqwqeXtDnwPmAD0Bo5raFnnXC1wD/6JxXvGKOuXxpiv6k0bbIwJG2MGBeMPBzVMG40xs4wxZzQS23XGmDfrTXvXGHNV1PhIY8w0Y8xaY8xSY8zNxpiURnb5h/hXNcQsM6q5Y0IQX5kx5lVjTDdjzF+MMWuCmrJfRq0/MWhW+L0xZmWwzB3RcWxvv40xexljXgv2Y33dfhtjZgSLvB7Umj3QwLHKNMbcGWyjyBjzgjFmQNT8d4OYngtiWGCMOamhgxS1T78xxiwP1rndGJMXlLHBGDM7unbDGJNsjLnGGLPQGFNsjHnLGDMyan6KMeavUcfw9zG2+z1jzIfBMVhgjPmdMSbuhN0Yc7IxZkZQuzjDGPOj+vtUb/lJdce0oWNtjFkc7NeHwXQxxuwXq4yoaYuNMWcaY/oAU4GkYN1NxpgJAM65Dfh3ep0Y7/6pbWlio1QH55xbB3wLPGCMGW+MGdGU//hjOB+Y6Zx7GV8TdEFDCxrf1PVLoAaYEWORJ4HdjDGjoqZNBN51zi0Oxj8ERgG5+CahScaYEc0J3BhTgH8lxGSgL77m6ijgj42sNhqYFUfxJwOH4F8iOQj4DFiAf2HiWcDfoxMH/IsnBwBDgjjGAZdHzW9wv40xvYP9eC/YVi/gLwDOub2D9Y92zmU5585tIN6/AQcGn4FAETDFbF0DNwG4A//6h7uBR4wxmY0cg4FBvEOCY3EJ/iR9G/51BJOB6FdcXA6MB44P9uED4A1jTE4w/w/ACcDBwOBgXwfWrRwcj1eD8nsAY/Gv1fh5IzFuZow5GHgi2E4ecCXwlDHmgHjW386xvhD4NdAd/+6tV6P2q7EyC/EXC+GgzCzn3CNRi3yN/02qZtLERqnEcDjwLnAp/oV+q40xV9dLcAYbY0qiP/jals2MMen4E1HdyelB4DizbefMPwXrL8e/NPNk59w2fXWcc8XAi/gTP0E8E4CHopZ50Dm3zjkXds79B5gZ7E9zjAdmOOfuc85VO+dWADcH0xvSDdgQR9k3OufWB4nky0CNc+7fzrla59xU/DuG9olaPgJc7pyrCJq5bsUndcB29/vnwHzn3M3OubJgX7aqqWqMMSaEP85XOedWOOfK8L+N3YH9oxZ92jn3sXMuAtyPT3CGNVJ0BXB9EM8MfDL7hXPuU+dcGP/m66HGmK7B8mcBtzjnZge1hzfg37U1Npg/Ppg/3zlXAVwGRL/n5xfAM865F4PjNBufgDX2fUabCDznnJsafE+vAM8DZ8e5fmMedM596ZyrBm7BH5sTWqDcDfhkSTWTJjZKJQDnXJFz7krn3Gj8FfUVwDUECUVgkXMuN/qDP3FEOxXIwp+gwF8trwXq1wr8X1BGgXPuYOfclEbCexg4I2iGOTKIbzL4E7Ax5gZjzJygqaAE2Bt/dd4cg4Ex9ZK3h/C1BQ0pxr98cXtWRg2X1xuvm5YdNb7GOVceNb4Y6Adx7fcgYG4cMTWkB/5FnIvqJjjnNuFfytk/armVUfPLgsHofahvTZAE1al/HOr2t66M/vViiOCPQ10M/YLx6BjWRJU3GDi93vd5Lb6JNB5bbT+wgK2PQXMtrhtw/qWLSwm+3x2Ug+/fpppJExulEoxzrtw5NwlfAzCqiaufj+8v840xZhW+RqYbDXcijscbQBW+KWYi8J/g6hzgdHzSdDLQLUi2ZtBwp+eNQJd60/pEDS8B3qyXwHUNOjo35H9As5q+tqOgXrPOIPzxhO3v92IarznZ3tuL1+KP+aC6CcaYLKAAWBZP8C1kWb0YQsF4XQwr6s3vwtZJ7RLgoXrfZ45zbo/mbD8wJGr72/s9QcPHOjpug292rPt+tyrXGJOMP/Z1opPD+kbif5OqmTSxUaqDM74T683Gd5pNCTpsnoz/D/KDJpQzAt9v4kf4hKjusz++xuP45sQXNFE8CvwK+DFRzVD4q9Na/Ik4ZIw5G19z0ZAvgdHGmH2D/bwYf1Vf51HAGmPONsakBzUjQ4wxxzZS5gvAD5q8Y9sXAm4xxmQYY4bgm1nq+lJsb78fB3Y1vvNxpjEm1RgTHeMqGkl8gpqRR4EbjTF9ggTrDmA28HkL7V88JgFXGGOGB/2x/gQkA68E8x8DLjfG7GKMycA310Wfl+4BfmqMGRf12x5hjDkszu0/ApxsjDnGGJNkjDkO/xusa2r9Cp+AnhD8Vn4EHFqvjIaO9dnGmNFBTeTlQGbUfn0JfN/4jvJpwP8B0R3YV+E7D0f/djHGZOP/3l6Kc/9UDJrYKNXxVeOvBifjq7DXAlcBv3LOPdOEci4ApjvnpjjnVkV9ZgLP0Egn4jg8DByGbw6LPrE+gu+EOx9/9T6CRpIx59y7wF+B1/BNID2Bj6LmrwKOwN/ptBjfzPQ8/iq9IY8BewfJR0tagr+CX4Tfx9fwJ27Yzn4HHUwPx3d8Xo4/EUZ3PP4TcIPxdxrd18D2fwMI/i6bpfjmmxODRLO13Ia/hfl1YDW+KfLo4O4f8P2fpgGf4o/TUvxxA8A59w2+38ql+O97DT5Ziqup0jn3Eb6v0e3438KtwJnOuU+D+QvwHYDvx//tHAs8V6+Yho71/cA/gnJPA8Y650qDeU/gk5Pp+KavpfjvuS6uucC/gM+DJra6ztCnA+845+bFs38qNuObBpVSqvMyxlwIjHHOxXW3TRzlTcR33NXnkSQgY8xi/Pf7+PaWbUKZacA3+OTzu5YqtzNKbusAlFKqrTnn7gXubes4VOcV3DXWWL8qFSdtilJKKaVUwtCmKKWUUkolDK2xUUoppVTC0MRGKaWUUglDExullFJKJQxNbJRSSimVMDSxUUoppVTC0MRGKaWUUgnj/wEKJDBN6bKEpQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x482.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(svm_explanation.shap_values[1], X_test_norm, feature_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that, indeed, a higher value of the `nonflavonoid_phenols` feature contributes to a sample being classified as class `1`, but that this effect is rather limited compared to features such as `proline` or `alcohol`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To visualise the impact of the feature across all classes, that is, the importance of a particular feature for the model, we simply pass all the shap values to the `summary_plot` functions. We see, that, for example, the `color_intensity` feature is much more important for deciding whether an instance should be classified as `class_2` then in `class_0`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x482.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(svm_explanation.shap_values, X_test_norm, feature_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dependence plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another way to visualise the model dependence on a particular feature is through a dependence plot. This plot shows the impact of the feature value on its importance for classification with respect to class `0`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature = 'flavanoids'\n",
    "shap.dependence_plot(\n",
    "    feature, \n",
    "    svm_explanation.shap_values[0], \n",
    "    X_test_norm, \n",
    "    feature_names=feature_names,\n",
    "    interaction_index='auto',\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The colour of the individual instances is represented by the value of the feature `nonflavanoid_phenols`. By specifying `interaction_index=auto`, the `nonflavanoid_phenols` was estimated as a the feature with the strongest interaction with the `flavanoids_feature`; this interaction is approximate, and is estimate by computing the Pearson Correlation Coefficient between the shap values of the reference feature (`flavanoids` in this case) and the value of each feature in turn on bins along the feature value. \n",
    "\n",
    "We see that, for class `0` wines, a higher value for `nonfavanoid_phenols` is generally associated with a low value in `flavanoids` and that they have a negative impact on the score for class `0` classification."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Footnotes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Footnotes'></a>\n",
    "\n",
    "[(a)](#f_1) The weights are applied to each point in a copy, so the number of weights should be the same as the number of samples in the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='References'></a>\n",
    "\n",
    "[[1]](#source_1) Lundberg, S.M. and Lee, S.I., 2017. A unified approach to interpreting model predictions. In Advances in neural information processing systems (pp. 4765-4774).\n",
    "\n",
    "[[2]](#source_2) Wikipedia entry on the Least-angle regression: <https://en.wikipedia.org/wiki/Least-angle_regression>."
   ]
  }
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